default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDNeurIPS 2018: Montréal, Canada
- Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, Roman Garnett:
Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. 2018 - Francis R. Bach:
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization. 1-10 - Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan:
Structure-Aware Convolutional Neural Networks. 11-20 - Guangrun Wang, Jiefeng Peng, Ping Luo, Xinjiang Wang, Liang Lin:
Kalman Normalization: Normalizing Internal Representations Across Network Layers. 21-31 - Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti:
HOGWILD!-Gibbs can be PanAccurate. 32-41 - Seonghyeon Nam, Yunji Kim, Seon Joo Kim:
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language. 42-51 - Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, Tieniu Tan:
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis. 52-63 - Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas:
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences. 64-75 - Tyler R. Scott, Karl Ridgeway, Michael C. Mozer:
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning. 76-85 - Chaosheng Dong, Yiran Chen, Bo Zeng:
Generalized Inverse Optimization through Online Learning. 86-95 - Ehsan Imani, Eric Graves, Martha White:
An Off-policy Policy Gradient Theorem Using Emphatic Weightings. 96-106 - Lei Le, Andrew Patterson, Martha White:
Supervised autoencoders: Improving generalization performance with unsupervised regularizers. 107-117 - Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, Bill Freeman:
Visual Object Networks: Image Generation with Disentangled 3D Representations. 118-129 - Yixi Xu, Xiao Wang:
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units. 130-139 - Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing:
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems. 140-151 - Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, Thomas Serre:
Learning long-range spatial dependencies with horizontal gated recurrent units. 152-164 - Zhisheng Zhong, Tiancheng Shen, Yibo Yang, Zhouchen Lin, Chao Zhang:
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution. 165-175 - Wenye Li, Jingwei Mao, Yin Zhang, Shuguang Cui:
Fast Similarity Search via Optimal Sparse Lifting. 176-184 - Karl Ridgeway, Michael C. Mozer:
Learning Deep Disentangled Embeddings With the F-Statistic Loss. 185-194 - Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller:
Geometrically Coupled Monte Carlo Sampling. 195-205 - Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation. 206-217 - Cong Han Lim:
An Efficient Pruning Algorithm for Robust Isotonic Regression. 218-227 - Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal:
PAC-learning in the presence of adversaries. 228-239 - Yiwen Guo, Chao Zhang, Changshui Zhang, Yurong Chen:
Sparse DNNs with Improved Adversarial Robustness. 240-249 - Celestine Dünner, Thomas P. Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Gummadi Ravi, Madhusudanan Kandasamy, Haralampos Pozidis:
Snap ML: A Hierarchical Framework for Machine Learning. 250-260 - Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li:
See and Think: Disentangling Semantic Scene Completion. 261-272 - Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong:
Chain of Reasoning for Visual Question Answering. 273-283 - Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka, Shuichi Adachi:
Sigsoftmax: Reanalysis of the Softmax Bottleneck. 284-294 - Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wangmeng Zuo, Wei Liu, Ming-Hsuan Yang:
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation. 295-305 - Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic:
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC. 306-317 - Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun:
MetaAnchor: Learning to Detect Objects with Customized Anchors. 318-328 - Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia:
Image Inpainting via Generative Multi-column Convolutional Neural Networks. 329-338 - Guangmo Amo Tong, Ding-Zhu Du, Weili Wu:
On Misinformation Containment in Online Social Networks. 339-349 - Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng:
A^2-Nets: Double Attention Networks. 350-359 - Pedro Morgado, Nuno Vasconcelos, Timothy R. Langlois, Oliver Wang:
Self-Supervised Generation of Spatial Audio for 360° Video. 360-370 - Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh:
How Many Samples are Needed to Estimate a Convolutional Neural Network? 371-381 - Simon S. Du, Wei Hu, Jason D. Lee:
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced. 382-393 - Yaron Singer, Avinatan Hassidim:
Optimization for Approximate Submodularity. 394-405 - Haggai Maron, Yaron Lipman:
(Probably) Concave Graph Matching. 406-416 - Ziang Yan, Yiwen Guo, Changshui Zhang:
Deep Defense: Training DNNs with Improved Adversarial Robustness. 417-426 - Junqi Tang, Mohammad Golbabaee, Francis R. Bach, Mike E. Davies:
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes. 427-438 - Mikhail Figurnov, Shakir Mohamed, Andriy Mnih:
Implicit Reparameterization Gradients. 439-450 - Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi:
Training DNNs with Hybrid Block Floating Point. 451-461 - Michael Mitzenmacher:
A Model for Learned Bloom Filters and Optimizing by Sandwiching. 462-471 - Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu, Jian Yin:
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis. 472-482 - Minhyuk Sung, Hao Su, Ronald Yu, Leonidas J. Guibas:
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions. 483-493 - Yunzhe Tao, Qi Sun, Qiang Du, Wei Liu:
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling. 494-504 - Ohad Shamir:
Are ResNets Provably Better than Linear Predictors? 505-514 - Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles:
Learning to Decompose and Disentangle Representations for Video Prediction. 515-524 - Ozan Sener, Vladlen Koltun:
Multi-Task Learning as Multi-Objective Optimization. 525-536 - Zhuwen Li, Qifeng Chen, Vladlen Koltun:
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. 537-546 - Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng:
Self-Erasing Network for Integral Object Attention. 547-557 - Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon:
LinkNet: Relational Embedding for Scene Graph. 558-568 - Boris Hanin, David Rolnick:
How to Start Training: The Effect of Initialization and Architecture. 569-579 - Boris Hanin:
Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? 580-589 - Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam
, Payel Das:
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. 590-601 - Peiqi Wang, Xinfeng Xie, Lei Deng, Guoqi Li, Dongsheng Wang, Yuan Xie:
HitNet: Hybrid Ternary Recurrent Neural Network. 602-612 - Christian Kroer, Tuomas Sandholm:
A Unified Framework for Extensive-Form Game Abstraction with Bounds. 613-624 - Zijun Zhang, Yining Zhang, Zongpeng Li:
Removing the Feature Correlation Effect of Multiplicative Noise. 625-634 - Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik:
Maximum-Entropy Fine Grained Classification. 635-645 - Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati:
On Learning Markov Chains. 646-655 - Bo Dai, Sanja Fidler, Dahua Lin:
A Neural Compositional Paradigm for Image Captioning. 656-666 - Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin:
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. 667-675 - Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogério Schmidt Feris:
Dialog-based Interactive Image Retrieval. 676-686 - Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang:
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. 687-697 - Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet:
Are GANs Created Equal? A Large-Scale Study. 698-707 - Emilien Dupont:
Learning Disentangled Joint Continuous and Discrete Representations. 708-718 - Boris N. Oreshkin, Pau Rodríguez López, Alexandre Lacoste:
TADAM: Task dependent adaptive metric for improved few-shot learning. 719-729 - Moran Feldman, Amin Karbasi, Ehsan Kazemi:
Do Less, Get More: Streaming Submodular Maximization with Subsampling. 730-740 - Rui Li, Kishan KC, Feng Cui, Justin Domke, Anne R. Haake:
Sparse Covariance Modeling in High Dimensions with Gaussian Processes. 741-750 - Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley J. Osher:
Deep Neural Nets with Interpolating Function as Output Activation. 751-761 - Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang:
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction. 762-772 - Ashish Kumar, Saurabh Gupta, David F. Fouhey, Sergey Levine, Jitendra Malik:
Visual Memory for Robust Path Following. 773-782 - Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi:
KDGAN: Knowledge Distillation with Generative Adversarial Networks. 783-794 - Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass:
Long short-term memory and Learning-to-learn in networks of spiking neurons. 795-805 - Shupeng Su, Chao Zhang, Kai Han, Yonghong Tian:
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN. 806-815 - Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. 816-827 - Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, Baoquan Chen:
PointCNN: Convolution On X-Transformed Points. 828-838 - Hu Liu, Sheng Jin, Changshui Zhang:
Connectionist Temporal Classification with Maximum Entropy Regularization. 839-849 - Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio:
Large Margin Deep Networks for Classification. 850-860 - Tianshu Yu, Junchi Yan, Yilin Wang, Wei Liu, Baoxin Li:
Generalizing Graph Matching beyond Quadratic Assignment Model. 861-871 - Christian Kroer, Gabriele Farina, Tuomas Sandholm:
Solving Large Sequential Games with the Excessive Gap Technique. 872-882 - Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jin-Hui Zhu:
Discrimination-aware Channel Pruning for Deep Neural Networks. 883-894 - Zi Yin, Yuanyuan Shen:
On the Dimensionality of Word Embedding. 895-906 - Ju Xu, Zhanxing Zhu:
Reinforced Continual Learning. 907-916 - Jay Heo, Haebeom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang:
Uncertainty-Aware Attention for Reliable Interpretation and Prediction. 917-926 - Haebeom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang:
DropMax: Adaptive Variational Softmax. 927-937 - Veronika Rocková, Nicholas Polson:
Posterior Concentration for Sparse Deep Learning. 938-949 - Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid:
A flexible model for training action localization with varying levels of supervision. 950-961 - Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan:
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents. 962-972 - Di Wang, Marco Gaboardi, Jinhui Xu:
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. 973-982 - Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang:
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks. 983-993 - Michel Deudon:
Learning semantic similarity in a continuous space. 994-1005 - Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa:
MetaReg: Towards Domain Generalization using Meta-Regularization. 1006-1016 - Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang:
Boosted Sparse and Low-Rank Tensor Regression. 1017-1026 - An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen:
Domain-Invariant Projection Learning for Zero-Shot Recognition. 1027-1038 - Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Josh Tenenbaum:
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. 1039-1050 - Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong:
Frequency-Domain Dynamic Pruning for Convolutional Neural Networks. 1051-1061 - Pan Li, Niao He, Olgica Milenkovic:
Quadratic Decomposable Submodular Function Minimization. 1062-1072 - Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon:
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. 1073-1083 - Lijun Zhang, Zhi-Hua Zhou:
\ell_1-regression with Heavy-tailed Distributions. 1084-1094 - Tobias Plötz, Stefan Roth:
Neural Nearest Neighbors Networks. 1095-1106 - Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett:
Efficient nonmyopic batch active search. 1107-1117 - Omer Ben-Porat, Moshe Tennenholtz:
A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers. 1118-1128 - Christopher Tosh, Sanjoy Dasgupta:
Interactive Structure Learning with Structural Query-by-Committee. 1129-1139 - Yanjun Li, Yoram Bresler:
Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere. 1140-1151 - Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Nikolai Yakovenko, Andrew Tao, Jan Kautz, Bryan Catanzaro:
Video-to-Video Synthesis. 1152-1164 - Zeyuan Allen-Zhu:
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD. 1165-1175 - Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha:
Synthesize Policies for Transfer and Adaptation across Tasks and Environments. 1176-1185 - Alhussein Fawzi, Hamza Fawzi, Omar Fawzi:
Adversarial vulnerability for any classifier. 1186-1195 - Shauharda Khadka, Kagan Tumer:
Evolution-Guided Policy Gradient in Reinforcement Learning. 1196-1208 - Sven Bambach, David J. Crandall, Linda B. Smith, Chen Yu:
Toddler-Inspired Visual Object Learning. 1209-1218 - Miguel Á. Carreira-Perpiñán, Pooya Tavallali:
Alternating optimization of decision trees, with application to learning sparse oblique trees. 1219-1229 - Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, Hongsheng Li:
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. 1230-1241 - Pan Zhou, Xiaotong Yuan, Jiashi Feng:
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity. 1242-1251 - Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang:
The Lingering of Gradients: How to Reuse Gradients Over Time. 1252-1261 - Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli:
Unsupervised Learning of View-invariant Action Representations. 1262-1272 - Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, Andreas Krause:
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making. 1273-1283 - Qilong Wang, Zilin Gao, Jiangtao Xie, Wangmeng Zuo, Peihua Li:
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks. 1284-1293 - Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio:
Image-to-image translation for cross-domain disentanglement. 1294-1305 - Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang:
Gradient Sparsification for Communication-Efficient Distributed Optimization. 1306-1316 - Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord:
Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection. 1317-1329 - Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou:
Adaptive Online Learning in Dynamic Environments. 1330-1340 - ChengYue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu:
FRAGE: Frequency-Agnostic Word Representation. 1341-1352 - Harshil Shah, David Barber:
Generative Neural Machine Translation. 1353-1362 - Austin R. Benson, Jon M. Kleinberg:
Found Graph Data and Planted Vertex Covers. 1363-1374 - Hajin Shim, Sung Ju Hwang, Eunho Yang:
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding. 1375-1385 - Ira Shavitt, Eran Segal:
Regularization Learning Networks: Deep Learning for Tabular Datasets. 1386-1396 - Alexis Bellot, Mihaela van der Schaar:
Multitask Boosting for Survival Analysis with Competing Risks. 1397-1406 - Ofir Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy:
Geometry Based Data Generation. 1407-1418 - Sumit Bam Shrestha, Garrick Orchard:
SLAYER: Spike Layer Error Reassignment in Time. 1419-1428 - Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Oracle-Efficient PAC RL with Rich Observations. 1429-1439 - Dongsung Huh, Terrence J. Sejnowski:
Gradient Descent for Spiking Neural Networks. 1440-1450 - Cheng Zhang, Frederick A. Matsen IV:
Generalizing Tree Probability Estimation via Bayesian Networks. 1451-1460 - Siddharth Reddy, Anca D. Dragan, Sergey Levine:
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior. 1461-1472 - Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang:
Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution. 1473-1482 - Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue:
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners. 1483-1493 - A loss framework for calibrated anomaly detection. 1494-1504
- Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh:
PacGAN: The power of two samples in generative adversarial networks. 1505-1514 - Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh:
Variational Memory Encoder-Decoder. 1515-1525 - Yunwen Lei, Ke Tang:
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities. 1526-1536 - Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing:
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation. 1537-1547 - Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee:
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization. 1548-1558 - Chenhan Jiang, Hang Xu, Xiaodan Liang, Liang Lin:
Hybrid Knowledge Routed Modules for Large-scale Object Detection. 1559-1570 - Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang:
Bilinear Attention Networks. 1571-1581 - Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, Qi Wu:
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning. 1582-1592 - Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang:
Multi-Class Learning: From Theory to Algorithm. 1593-1602 - Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, Xiaojie Yuan:
Multivariate Time Series Imputation with Generative Adversarial Networks. 1603-1614 - Yunhe Wang, Chang Xu, Chunjing Xu, Chao Xu, Dacheng Tao:
Learning Versatile Filters for Efficient Convolutional Neural Networks. 1615-1625 - Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich:
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. 1626-1636 - Peng Jiang, Fanglin Gu, Yunhai Wang, Changhe Tu, Baoquan Chen:
DifNet: Semantic Segmentation by Diffusion Networks. 1637-1646 - Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Conditional Adversarial Domain Adaptation. 1647-1657 - Ignacio Rocco, Mircea Cimpoi, Relja Arandjelovic, Akihiko Torii, Tomás Pajdla, Josef Sivic:
Neighbourhood Consensus Networks. 1658-1669 - Edouard Pauwels, Francis R. Bach, Jean-Philippe Vert:
Relating Leverage Scores and Density using Regularized Christoffel Functions. 1670-1679 - Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang:
Non-Local Recurrent Network for Image Restoration. 1680-1689 - Yin Cheng Ng, Nicolò Colombo, Ricardo Silva:
Bayesian Semi-supervised Learning with Graph Gaussian Processes. 1690-1701 - Abhishek Sharma:
Foreground Clustering for Joint Segmentation and Localization in Videos and Images. 1702-1711 - Jingwei Xu, Bingbing Ni, Xiaokang Yang:
Video Prediction via Selective Sampling. 1712-1722 - Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin:
Distilled Wasserstein Learning for Word Embedding and Topic Modeling. 1723-1732 - Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu:
Learning to Exploit Stability for 3D Scene Parsing. 1733-1743 - Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel:
Neural Guided Constraint Logic Programming for Program Synthesis. 1744-1753 - Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak:
Genetic-Gated Networks for Deep Reinforcement Learning. 1754-1763 - Romain Warlop, Alessandro Lazaric, Jérémie Mary:
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning. 1764-1773 - Isabel Valera, Adish Singla, Manuel Gomez Rodriguez:
Enhancing the Accuracy and Fairness of Human Decision Making. 1774-1783 - Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup:
Temporal Regularization for Markov Decision Process. 1784-1794 - Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. 1795-1804 - Horia Mania, Aurelia Guy, Benjamin Recht:
Simple random search of static linear policies is competitive for reinforcement learning. 1805-1814 - Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan:
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization. 1815-1825 - Marylou Gabrié, Andre Manoel, Clément Luneau, Jean Barbier, Nicolas Macris, Florent Krzakala, Lenka Zdeborová:
Entropy and mutual information in models of deep neural networks. 1826-1836 - Guocong Song, Wei Chai:
Collaborative Learning for Deep Neural Networks. 1837-1846 - Ilias Zadik, David Gamarnik:
High Dimensional Linear Regression using Lattice Basis Reduction. 1847-1857 - Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing:
Symbolic Graph Reasoning Meets Convolutions. 1858-1868 - Arash Vahdat, Evgeny Andriyash, William G. Macready:
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors. 1869-1878 - Peter Anderson, Stephen Gould, Mark Johnson:
Partially-Supervised Image Captioning. 1879-1890 - Shunyu Yao, Tzu-Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, Bill Freeman, Josh Tenenbaum:
3D-Aware Scene Manipulation via Inverse Graphics. 1891-1902 - Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. 1903-1913 - Ashok Cutkosky, Róbert Busa-Fekete:
Distributed Stochastic Optimization via Adaptive SGD. 1914-1923 - Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich:
Precision and Recall for Time Series. 1924-1934 - Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang:
Deep Attentive Tracking via Reciprocative Learning. 1935-1945 - Binghui Chen, Weihong Deng, Haifeng Shen:
Virtual Class Enhanced Discriminative Embedding Learning. 1946-1956 - Liang Zhang, Guangming Zhu, Lin Mei, Peiyi Shen, Syed Afaq Ali Shah, Mohammed Bennamoun:
Attention in Convolutional LSTM for Gesture Recognition. 1957-1966 - Jun Wang, Tanner A. Bohn, Charles X. Ling:
Pelee: A Real-Time Object Detection System on Mobile Devices. 1967-1976 - Simina Brânzei, Ruta Mehta, Noam Nisan:
Universal Growth in Production Economies. 1975 - Fei Jiang, Guosheng Yin, Francesca Dominici:
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors. 1978-1987 - Pan Zhou, Xiaotong Yuan, Jiashi Feng:
Efficient Stochastic Gradient Hard Thresholding. 1988-1997 - Cem Keskin, Shahram Izadi:
SplineNets: Continuous Neural Decision Graphs. 1998-2008 - Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Generalized Zero-Shot Learning with Deep Calibration Network. 2009-2019 - Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. 2020-2029 - William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec:
Embedding Logical Queries on Knowledge Graphs. 2030-2041 - Jinyan Liu, Zhiyi Huang, Xiangning Wang:
Learning Optimal Reserve Price against Non-myopic Bidders. 2042-2052 - Lu Qi, Shu Liu, Jianping Shi, Jiaya Jia:
Sequential Context Encoding for Duplicate Removal. 2053-2062 - Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi:
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning. 2063-2074 - Simon Lyddon, Stephen Walker, Chris C. Holmes:
Nonparametric learning from Bayesian models with randomized objective functions. 2075-2085 - Filip Hanzely, Konstantin Mishchenko, Peter Richtárik:
SEGA: Variance Reduction via Gradient Sketching. 2086-2097 - Amit Zohar, Lior Wolf:
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector. 2098-2107 - Sagie Benaim, Lior Wolf:
One-Shot Unsupervised Cross Domain Translation. 2108-2118 - Etai Littwin, Lior Wolf:
Regularizing by the Variance of the Activations' Sample-Variances. 2119-2129 - Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti:
Overlapping Clustering Models, and One (class) SVM to Bind Them All. 2130-2140 - Markus Lange-Hegermann:
Algorithmic Linearly Constrained Gaussian Processes. 2141-2152 - Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, Bo Zhang:
DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning. 2153-2163 - Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry:
Norm matters: efficient and accurate normalization schemes in deep networks. 2164-2174 - Zhihui Zhu, Yifan Wang, Daniel P. Robinson, Daniel Q. Naiman, René Vidal, Manolis C. Tsakiris:
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms. 2175-2185 - Helena Peic Tukuljac, Antoine Deleforge, Rémi Gribonval:
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval. 2186-2196 - Daniel L. Pimentel-Alarcón:
Mixture Matrix Completion. 2197-2207 - Yue Zhao, Yuanjun Xiong, Dahua Lin:
Trajectory Convolution for Action Recognition. 2208-2219 - Léonard Blier, Yann Ollivier:
The Description Length of Deep Learning models. 2220-2230 - Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. 2231-2241 - Pan Li, Olgica Milenkovic:
Revisiting Decomposable Submodular Function Minimization with Incidence Relations. 2242-2252 - Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang:
A Practical Algorithm for Distributed Clustering and Outlier Detection. 2253-2262 - Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, Jiajun Wu:
Learning to Reconstruct Shapes from Unseen Classes. 2263-2274 - Chang Xiao, Peilin Zhong, Changxi Zheng:
BourGAN: Generative Networks with Metric Embeddings. 2275-2286 - Thomas Pumir, Samy Jelassi, Nicolas Boumal:
Smoothed analysis of the low-rank approach for smooth semidefinite programs. 2287-2296 - Ofir Marom, Benjamin Rosman:
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning. 2297-2305 - Mikhail Belkin, Daniel J. Hsu, Partha Mitra:
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. 2306-2317 - Robert Hannah, Yanli Liu, Daniel O'Connor, Wotao Yin:
Breaking the Span Assumption Yields Fast Finite-Sum Minimization. 2318-2327 - Yuqian Zhang, Han-Wen Kuo, John Wright:
Structured Local Minima in Sparse Blind Deconvolution. 2328-2337 - Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney:
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. 2338-2348 - Xenia Miscouridou, Francois Caron, Yee Whye Teh:
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data. 2349-2358 - Eric Balkanski, Adam Breuer, Yaron Singer:
Non-monotone Submodular Maximization in Exponentially Fewer Iterations. 2359-2370 - Ruixiang Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song:
MetaGAN: An Adversarial Approach to Few-Shot Learning. 2371-2380 - Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. 2381-2390 - Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth:
Gaussian Process Conditional Density Estimation. 2391-2401 - Zhongwen Xu, Hado van Hasselt, David Silver:
Meta-Gradient Reinforcement Learning. 2402-2413 - Louis Kirsch, Julius Kunze, David Barber:
Modular Networks: Learning to Decompose Neural Computation. 2414-2423 - Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell:
Learning to Navigate in Cities Without a Map. 2424-2435 - Kuang Xu:
Query Complexity of Bayesian Private Learning. 2436-2445 - Cédric Josz, Yi Ouyang, Richard Y. Zhang, Javad Lavaei, Somayeh Sojoudi:
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization. 2446-2454 - David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. 2455-2467 - Shannon R. McCurdy:
Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling. 2468-2477 - Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris:
Wasserstein Variational Inference. 2478-2487 - Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry:
How Does Batch Normalization Help Optimization? 2488-2498 - Osbert Bastani, Yewen Pu, Armando Solar-Lezama:
Verifiable Reinforcement Learning via Policy Extraction. 2499-2509 - Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu:
Leveraged volume sampling for linear regression. 2510-2519 - Gregory Plumb, Denali Molitor, Ameet Talwalkar:
Model Agnostic Supervised Local Explanations. 2520-2529 - Peng Jiang, Gagan Agrawal:
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. 2530-2541 - Jack Goetz, Ambuj Tewari, Paul M. Zimmerman:
Active Learning for Non-Parametric Regression Using Purely Random Trees. 2542-2551 - Xinyun Chen, Chang Liu, Dawn Song:
Tree-to-tree Neural Networks for Program Translation. 2552-2562 - Hyeonseob Nam, Hyo-Eun Kim:
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks. 2563-2572 - Sanghack Lee, Elias Bareinboim:
Structural Causal Bandits: Where to Intervene? 2573-2583 - Sang-Woo Lee, Yu-Jung Heo, Byoung-Tak Zhang:
Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog. 2584-2594 - Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh, Yu-Chiang Frank Wang:
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation. 2595-2604 - Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. 2605-2614 - Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud:
Isolating Sources of Disentanglement in Variational Autoencoders. 2615-2625 - Dylan J. Foster, Akshay Krishnamurthy:
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. 2626-2637 - Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang:
Representation Learning for Treatment Effect Estimation from Observational Data. 2638-2648 - Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill:
Representation Balancing MDPs for Off-policy Policy Evaluation. 2649-2658 - Medhini Narasimhan, Svetlana Lazebnik, Alexander G. Schwing:
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. 2659-2670 - Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery from Discrete Data using Hidden Compact Representation. 2671-2679 - Zeyuan Allen-Zhu:
Natasha 2: Faster Non-Convex Optimization Than SGD. 2680-2691 - Jaeho Lee, Maxim Raginsky:
Minimax Statistical Learning with Wasserstein distances. 2692-2701 - Josip Djolonga, Stefanie Jegelka, Andreas Krause:
Provable Variational Inference for Constrained Log-Submodular Models. 2702-2712 - Seunghoon Hong, Xinchen Yan, Thomas E. Huang, Honglak Lee:
Learning Hierarchical Semantic Image Manipulation through Structured Representations. 2713-2723 - Marek Smieja, Lukasz Struski, Jacek Tabor, Bartosz Zielinski, Przemyslaw Spurek:
Processing of missing data by neural networks. 2724-2734 - Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong:
Safe Active Learning for Time-Series Modeling with Gaussian Processes. 2735-2744 - Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee:
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. 2745-2754 - Sören Laue, Matthias Mitterreiter, Joachim Giesen:
Computing Higher Order Derivatives of Matrix and Tensor Expressions. 2755-2764 - Jangho Kim, Seonguk Park, Nojun Kwak:
Paraphrasing Complex Network: Network Compression via Factor Transfer. 2765-2774 - Tom Michoel:
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net. 2775-2785 - Chaitanya Ryali, Gautam Reddy, Angela J. Yu:
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation. 2786-2795 - Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization Under Fairness Constraints. 2796-2806 - Eldar Insafutdinov, Alexey Dosovitskiy:
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds. 2807-2817 - Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama:
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. 2818-2829 - Zhiqiang Xu:
Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation. 2830-2839 - Julian Zimmert, Yevgeny Seldin:
Factored Bandits. 2840-2849 - Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein:
Delta-encoder: an effective sample synthesis method for few-shot object recognition. 2850-2860 - Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara:
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators. 2861-2871 - Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, Zhenan Sun:
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization. 2872-2882 - Ya-Ping Hsieh, Ali Kavis, Paul Rolland, Volkan Cevher:
Mirrored Langevin Dynamics. 2883-2892 - Elliot J. Crowley, Gavin Gray, Amos J. Storkey:
Moonshine: Distilling with Cheap Convolutions. 2893-2903 - Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. 2904-2913 - Tobias Sommer Thune, Yevgeny Seldin:
Adaptation to Easy Data in Prediction with Limited Advice. 2914-2923 - Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Inference for Exponential Families. 2924-2934 - Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas:
Playing hard exploration games by watching YouTube. 2935-2945 - Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin:
Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base. 2946-2955 - Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng:
Norm-Ranging LSH for Maximum Inner Product Search. 2956-2965 - Dimitris Bertsimas, Christopher McCord:
Optimization over Continuous and Multi-dimensional Decisions with Observational Data. 2966-2974 - Flavio V. D. de Figueiredo, Guilherme Resende Borges, Pedro O. S. Vaz de Melo, Renato M. Assunção:
Fast Estimation of Causal Interactions using Wold Processes. 2975-2986 - Cheng Tang, Damien Garreau, Ulrike von Luxburg:
When do random forests fail? 2987-2997 - Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. 2998-3008 - Jean-Bastien Grill, Michal Valko, Rémi Munos:
Optimistic optimization of a Brownian. 3009-3018 - Debarghya Ghoshdastidar, Ulrike von Luxburg:
Practical Methods for Graph Two-Sample Testing. 3019-3028 - Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino J. Gomez:
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations. 3029-3039 - Lénaïc Chizat, Francis R. Bach:
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport. 3040-3050 - Raanan Y. Yehezkel Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik:
Constructing Deep Neural Networks by Bayesian Network Structure Learning. 3051-3062 - Xuguang Duan, Wen-bing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang:
Weakly Supervised Dense Event Captioning in Videos. 3063-3073 - Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood:
Faithful Inversion of Generative Models for Effective Amortized Inference. 3074-3084 - Hao Cui, Radu Marinescu, Roni Khardon:
From Stochastic Planning to Marginal MAP. 3085-3095 - Hamid Jalalzai, Stéphan Clémençon, Anne Sabourin:
On Binary Classification in Extreme Regions. 3096-3104 - Yining Wang, Xi Chen, Yuan Zhou:
Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models. 3105-3114 - Devavrat Shah, Qiaomin Xie:
Q-learning with Nearest Neighbors. 3115-3125 - Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. 3126-3137 - François Portier, Bernard Delyon:
Asymptotic optimality of adaptive importance sampling. 3138-3148 - Kevin Bello, Jean Honorio:
Learning latent variable structured prediction models with Gaussian perturbations. 3149-3159 - Jiantao Jiao, Weihao Gao, Yanjun Han:
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal. 3160-3171 - Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez:
Deep Reinforcement Learning of Marked Temporal Point Processes. 3172-3182 - Murat Sensoy, Lance M. Kaplan, Melih Kandemir:
Evidential Deep Learning to Quantify Classification Uncertainty. 3183-3193 - Mingyuan Zhou:
Parsimonious Bayesian deep networks. 3194-3204 - Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber:
Single-Agent Policy Tree Search With Guarantees. 3205-3215 - Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang:
Semi-crowdsourced Clustering with Deep Generative Models. 3216-3226 - Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová:
The committee machine: Computational to statistical gaps in learning a two-layers neural network. 3227-3238 - Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. 3239-3250 - Lixing Chen, Jie Xu, Zhuo Lu:
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward. 3251-3260 - Shakarim Soltanayev, Se Young Chun:
Training deep learning based denoisers without ground truth data. 3261-3271 - David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel:
Re-evaluating evaluation. 3272-3283 - Oisín Moran, Piergiorgio Caramazza, Daniele Faccio, Roderick Murray-Smith:
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres. 3284-3295 - Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort:
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. 3296-3306 - Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine:
Data-Efficient Hierarchical Reinforcement Learning. 3307-3317 - Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, Trevor Darrell:
Speaker-Follower Models for Vision-and-Language Navigation. 3318-3329 - Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel:
Inequity aversion improves cooperation in intertemporal social dilemmas. 3330-3340 - David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch:
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. 3341-3351 - Nicoló Fusi, Rishit Sheth, Melih Elibol:
Probabilistic Matrix Factorization for Automated Machine Learning. 3352-3361 - Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov:
Stochastic Spectral and Conjugate Descent Methods. 3362-3371 - Rasmus Berg Palm, Ulrich Paquet, Ole Winther:
Recurrent Relational Networks. 3372-3382 - Yitong Sun, Anna C. Gilbert, Ambuj Tewari:
But How Does It Work in Theory? Linear SVM with Random Features. 3383-3392 - Tianqi Chen, Lianmin Zheng, Eddie Q. Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy:
Learning to Optimize Tensor Programs. 3393-3404 - Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch:
Boosting Black Box Variational Inference. 3405-3415 - Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan:
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. 3416-3425 - Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling:
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. 3426-3439 - Kamil Nar, Shankar Sastry:
Step Size Matters in Deep Learning. 3440-3448 - Yu Liu, Kris De Brabanter:
Derivative Estimation in Random Design. 3449-3458 - Krishnakumar Balasubramanian, Saeed Ghadimi:
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. 3459-3468 - Daniel D. Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit:
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments. 3469-3478 - Jing Li, Rafal Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet:
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation. 3479-3489 - Arno Solin, James Hensman, Richard E. Turner:
Infinite-Horizon Gaussian Processes. 3490-3499 - Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. 3500-3510 - Zijun Wei, Boyu Wang, Minh Hoai Nguyen, Jianming Zhang, Zhe L. Lin, Xiaohui Shen, Radomír Mech, Dimitris Samaras:
Sequence-to-Segment Networks for Segment Detection. 3511-3520 - David M. Zoltowski, Jonathan W. Pillow:
Scaling the Poisson GLM to massive neural datasets through polynomial approximations. 3521-3531 - Yun Kuen Cheung:
Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games. 3532-3542 - Irene Y. Chen, Fredrik D. Johansson, David A. Sontag:
Why Is My Classifier Discriminatory? 3543-3554 - Ji Feng, Yang Yu, Zhi-Hua Zhou:
Multi-Layered Gradient Boosting Decision Trees. 3555-3565 - Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. 3566-3577 - Bayan Saparbayeva, Michael Minyi Zhang, Lizhen Lin:
Communication Efficient Parallel Algorithms for Optimization on Manifolds. 3578-3588 - Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler:
Neural Code Comprehension: A Learnable Representation of Code Semantics. 3589-3601 - Jiecao Chen, Qin Zhang, Yuan Zhou:
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights. 3602-3611 - Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzcinski:
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN. 3612-3622 - Matthew Olson, Abraham J. Wyner, Richard Berk:
Modern Neural Networks Generalize on Small Data Sets. 3623-3632 - Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie:
Escaping Saddle Points in Constrained Optimization. 3633-3643 - Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin (Jerry) Zhu:
Adversarial Attacks on Stochastic Bandits. 3644-3653 - Daniel Ting, Eric Brochu:
Optimal Subsampling with Influence Functions. 3654-3663 - Kevin G. Jamieson, Lalit Jain:
A Bandit Approach to Sequential Experimental Design with False Discovery Control. 3664-3674 - Junzhe Zhang, Elias Bareinboim:
Equality of Opportunity in Classification: A Causal Approach. 3675-3685 - Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao:
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. 3686-3696 - Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim:
Unsupervised Attention-guided Image-to-Image Translation. 3697-3707 - Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis:
Inferring Networks From Random Walk-Based Node Similarities. 3708-3719 - Zeyuan Allen-Zhu, Yuanzhi Li:
NEON2: Finding Local Minima via First-Order Oracles. 3720-3730 - Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini:
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization. 3731-3741 - Hippolyt Ritter, Aleksandar Botev, David Barber:
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. 3742-3752 - Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. 3753-3763 - Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. 3764-3773 - Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler:
Direct Estimation of Differences in Causal Graphs. 3774-3785 - Ainesh Bakshi, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Distance Matrices. 3786-3796 - Ganesh Sundaramoorthi, Anthony J. Yezzi:
Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms. 3797-3807 - Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li:
Bayesian Inference of Temporal Task Specifications from Demonstrations. 3808-3817 - Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, M. K. Ryu, Greg Imwalle:
Data center cooling using model-predictive control. 3818-3827 - Jun-Kun Wang, Jacob D. Abernethy:
Acceleration through Optimistic No-Regret Dynamics. 3828-3838 - Aladin Virmaux, Kevin Scaman:
Lipschitz regularity of deep neural networks: analysis and efficient estimation. 3839-3848 - Kaiyi Ji, Yingbin Liang:
Minimax Estimation of Neural Net Distance. 3849-3858 - Pierre-Alexandre Mattei, Jes Frellsen:
Leveraging the Exact Likelihood of Deep Latent Variable Models. 3859-3870 - Stefan Neumann:
Bipartite Stochastic Block Models with Tiny Clusters. 3871-3881 - Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini:
Learning sparse neural networks via sensitivity-driven regularization. 3882-3892 - Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang:
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. 3893-3903 - Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie:
Direct Runge-Kutta Discretization Achieves Acceleration. 3904-3913 - Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. 3914-3924 - Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. 3925-3936 - Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski:
Faster Neural Networks Straight from JPEG. 3937-3948 - Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári:
TopRank: A practical algorithm for online stochastic ranking. 3949-3958 - Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato:
Learning from discriminative feature feedback. 3959-3967 - Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, Arye Nehorai:
RetGK: Graph Kernels based on Return Probabilities of Random Walks. 3968-3978 - Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé:
Deep Generative Markov State Models. 3979-3988 - Meimei Liu, Guang Cheng:
Early Stopping for Nonparametric Testing. 3989-3998 - Xiaohan Wei, Hao Yu, Qing Ling, Michael J. Neely:
Solving Non-smooth Constrained Programs with Lower Complexity than \mathcal{O}(1/\varepsilon): A Primal-Dual Homotopy Smoothing Approach. 3999-4009 - Joshua Fromm, Shwetak N. Patel, Matthai Philipose:
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks. 4010-4019 - Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi:
Unsupervised Learning of Object Landmarks through Conditional Image Generation. 4020-4031 - Zhiwei Deng, Jiacheng Chen, Yifang Fu, Greg Mori:
Probabilistic Neural Programmed Networks for Scene Generation. 4032-4042 - Volker Fischer, Jan Köhler, Thomas Pfeil:
The streaming rollout of deep networks - towards fully model-parallel execution. 4043-4054 - Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic:
KONG: Kernels for ordered-neighborhood graphs. 4055-4064 - Amir H. Khoshaman, Mohammad H. Amin:
GumBolt: Extending Gumbel trick to Boltzmann priors. 4065-4074 - Martin Magill, Faisal Z. Qureshi, Hendrick W. de Haan:
Neural Networks Trained to Solve Differential Equations Learn General Representations. 4075-4085 - Chaitanya Ryali, Angela J. Yu:
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account. 4086-4096 - Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satrajit S. Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira:
Distributed Weight Consolidation: A Brain Segmentation Case Study. 4097-4107 - Bei Jia, Surjyendu Ray, Sam Safavi, José Bento:
Efficient Projection onto the Perfect Phylogeny Model. 4108-4118 - Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang, Yuan Xie:
TETRIS: TilE-matching the TRemendous Irregular Sparsity. 4119-4129 - Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru:
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification. 4130-4140 - Raman Arora, Vladimir Braverman, Jalaj Upadhyay:
Differentially Private Robust Low-Rank Approximation. 4141-4149 - Tongzhou Wang, Yi Wu, Dave Moore, Stuart J. Russell:
Meta-Learning MCMC Proposals. 4150-4160 - Shi Dong, Benjamin Van Roy:
An Information-Theoretic Analysis for Thompson Sampling with Many Actions. 4161-4169 - Eszter Vértes, Maneesh Sahani:
Flexible and accurate inference and learning for deep generative models. 4170-4179 - Jalaj Upadhyay:
The Price of Privacy for Low-rank Factorization. 4180-4191 - Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu:
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. 4192-4201 - Jie Xu, Lei Luo, Cheng Deng, Heng Huang:
Bilevel Distance Metric Learning for Robust Image Recognition. 4202-4211 - Jordan Awan, Aleksandra B. Slavkovic:
Differentially Private Uniformly Most Powerful Tests for Binomial Data. 4212-4222 - Maria Dimakopoulou, Ian Osband, Benjamin Van Roy:
Scalable Coordinated Exploration in Concurrent Reinforcement Learning. 4223-4232 - Amir Dezfouli, Richard W. Morris, Fabio T. Ramos, Peter Dayan, Bernard W. Balleine:
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. 4233-4242 - Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang, Shuai Wang, Shu-Tao Xia, Jianping Wu:
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training. 4243-4253 - Hiroyuki Kasai, Bamdev Mishra:
Inexact trust-region algorithms on Riemannian manifolds. 4254-4265 - Nitin Bansal, Xiaohan Chen, Zhangyang Wang:
Can We Gain More from Orthogonality Regularizations in Training Deep Networks? 4266-4276 - Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh:
Binary Rating Estimation with Graph Side Information. 4277-4288 - Seyed Mehran Kazemi, David Poole:
SimplE Embedding for Link Prediction in Knowledge Graphs. 4289-4300 - Roshan Shariff, Or Sheffet:
Differentially Private Contextual Linear Bandits. 4301-4311 - Abram L. Friesen, Pedro M. Domingos:
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing. 4312-4322 - Risheng Liu, Shichao Cheng, Xiaokun Liu, Long Ma, Xin Fan, Zhongxuan Luo:
A Bridging Framework for Model Optimization and Deep Propagation. 4323-4332 - Nan Jiang, Alex Kulesza, Satinder Singh:
Completing State Representations using Spectral Learning. 4333-4342 - Yining Wang, Sivaraman Balakrishnan, Aarti Singh:
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. 4343-4354 - Shiyu Liang, Ruoyu Sun, Jason D. Lee, R. Srikant:
Adding One Neuron Can Eliminate All Bad Local Minima. 4355-4365 - Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi:
Mean-field theory of graph neural networks in graph partitioning. 4366-4376 - Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. 4377-4386 - Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi:
Mallows Models for Top-k Lists. 4387-4397 - Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon:
Amortized Inference Regularization. 4398-4407 - Kyungjae Lee, Sungjoon Choi, Songhwai Oh:
Maximum Causal Tsallis Entropy Imitation Learning. 4408-4418 - Song Zhou, Swati Gupta, Madeleine Udell:
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives. 4419-4429 - Haitian Sun, William W. Cohen, Lidong Bing:
Semi-Supervised Learning with Declaratively Specified Entropy Constraints. 4430-4440 - Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E:
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. 4441-4451 - Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi:
Sparsified SGD with Memory. 4452-4463 - Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka:
Exponentiated Strongly Rayleigh Distributions. 4464-4474 - Justin Domke, Daniel Sheldon:
Importance Weighting and Variational Inference. 4475-4484 - Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio López-Moreno, Yonghui Wu:
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. 4485-4495 - Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish Sabharwal:
Expanding Holographic Embeddings for Knowledge Completion. 4496-4506 - Jorge A. Mendez, Shashank Shivkumar, Eric Eaton:
Lifelong Inverse Reinforcement Learning. 4507-4518 - Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin:
Explaining Deep Learning Models - A Bayesian Non-parametric Approach. 4519-4529 - Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. 4530-4540 - Lie He, An Bian, Martin Jaggi:
COLA: Decentralized Linear Learning. 4541-4551 - Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun:
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare. 4552-4562 - Wen-bing Huang, Tong Zhang, Yu Rong, Junzhou Huang:
Adaptive Sampling Towards Fast Graph Representation Learning. 4563-4572 - Samuel Yeom, Anupam Datta, Matt Fredrikson:
Hunting for Discriminatory Proxies in Linear Regression Models. 4573-4583 - Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu:
Towards Robust Detection of Adversarial Examples. 4584-4594 - Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin, Rynson W. H. Lau:
Active Matting. 4595-4605 - Haidar Khan, Bülent Yener:
Learning filter widths of spectral decompositions with wavelets. 4606-4617 - Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li:
Byzantine Stochastic Gradient Descent. 4618-4628 - Bianca Dumitrascu, Karen Feng, Barbara E. Engelhardt:
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits. 4629-4638 - Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang:
Spectral Filtering for General Linear Dynamical Systems. 4639-4648 - Zeyu Zheng, Junhyuk Oh, Satinder Singh:
On Learning Intrinsic Rewards for Policy Gradient Methods. 4649-4659 - Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Boolean Decision Rules via Column Generation. 4660-4670 - Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin:
Adversarial Text Generation via Feature-Mover's Distance. 4671-4682 - Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. 4683-4694 - Shahin Shahrampour, Vahid Tarokh:
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels. 4695-4706 - Malik Magdon-Ismail, Lirong Xia:
A Mathematical Model For Optimal Decisions In A Representative Democracy. 4707-4716 - Nishant Desai, Andrew Critch, Stuart J. Russell:
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making. 4717-4725 - Stanislav Morozov, Artem Babenko:
Non-metric Similarity Graphs for Maximum Inner Product Search. 4726-4735 - Yi Tay, Anh Tuan Luu, Siu Cheung Hui:
Recurrently Controlled Recurrent Networks. 4736-4748 - Kaito Fujii, Tasuku Soma:
Fast greedy algorithms for dictionary selection with generalized sparsity constraints. 4749-4758 - Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine:
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. 4759-4770 - Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui:
A Smoother Way to Train Structured Prediction Models. 4771-4783 - Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White:
Context-dependent upper-confidence bounds for directed exploration. 4784-4794 - Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, Pawan Kumar Mudigonda:
A Unified View of Piecewise Linear Neural Network Verification. 4795-4804 - Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec:
Hierarchical Graph Representation Learning with Differentiable Pooling. 4805-4815 - Quoc Tran-Dinh:
Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates. 4816-4824 - Boyla Mainsah, Dmitry Kalika, Leslie M. Collins, Siyuan Liu, Chandra S. Throckmorton:
Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces. 4825-4835 - Soheil Feizi, Hamid Javadi, Jesse M. Zhang, David Tse:
Porcupine Neural Networks: Approximating Neural Network Landscapes. 4836-4846 - Michael P. Kim, Omer Reingold, Guy N. Rothblum:
Fairness Through Computationally-Bounded Awareness. 4847-4857 - Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang:
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. 4858-4867 - Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-Learning Provably Efficient? 4868-4878 - Xin Zhang, Armando Solar-Lezama, Rishabh Singh:
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. 4879-4890 - Leena Chennuru Vankadara, Ulrike von Luxburg:
Measures of distortion for machine learning. 4891-4900 - Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
On the Local Minima of the Empirical Risk. 4901-4910 - Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su:
Densely Connected Attention Propagation for Reading Comprehension. 4911-4922 - Virag Shah, Jose H. Blanchet, Ramesh Johari:
Bandit Learning with Positive Externalities. 4923-4933 - Wenbo Wang, Xingye Qiao:
Learning Confidence Sets using Support Vector Machines. 4934-4943 - Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel:
Efficient Neural Network Robustness Certification with General Activation Functions. 4944-4953 - Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney:
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. 4954-4964 - Satoshi Koide, Keisuke Kawano, Takuro Kutsuna:
Neural Edit Operations for Biological Sequences. 4965-4975 - Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima:
Objective and efficient inference for couplings in neuronal networks. 4976-4985 - Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh:
Learning from Group Comparisons: Exploiting Higher Order Interactions. 4986-4995 - Vikas K. Garg:
Supervising Unsupervised Learning. 4996-5006 - Quan Zhang, Mingyuan Zhou:
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks. 5007-5018 - Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry:
Adversarially Robust Generalization Requires More Data. 5019-5031 - Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, Jeff Clune:
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents. 5032-5043 - Gabriele Farina, Nicola Gatti, Tuomas Sandholm:
Practical exact algorithm for trembling-hand equilibrium refinements in games. 5044-5054 - Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin:
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning. 5055-5065 - Quanming Yao, James T. Kwok:
Scalable Robust Matrix Factorization with Nonconvex Loss. 5066-5075 - Michael J. Morais, Jonathan W. Pillow:
Power-law efficient neural codes provide general link between perceptual bias and discriminability. 5076-5085 - Ricson Cheng, Ziyan Wang, Katerina Fragkiadaki:
Geometry-Aware Recurrent Neural Networks for Active Visual Recognition. 5086-5096 - Ayush Jaiswal, Rex Yue Wu, Wael Abd-Almageed, Prem Natarajan:
Unsupervised Adversarial Invariance. 5097-5107 - Lajanugen Logeswaran, Honglak Lee, Samy Bengio:
Content preserving text generation with attribute controls. 5108-5118 - Siwei Wang, Longbo Huang:
Multi-armed Bandits with Compensation. 5119-5128 - Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander G. Schwing, Murali Annavaram, Salman Avestimehr:
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training. 5129-5139 - Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye:
Learning in Games with Lossy Feedback. 5140-5150 - Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry:
Scalable methods for 8-bit training of neural networks. 5151-5159 - Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li:
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization. 5160-5170 - Muhan Zhang, Yixin Chen:
Link Prediction Based on Graph Neural Networks. 5171-5181 - Dalin Guo, Angela J. Yu:
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task. 5182-5191 - Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, Yinyu Ye:
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. 5192-5202 - Hongyang Gao, Zhengyang Wang, Shuiwang Ji:
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. 5203-5211 - Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Lai-Wan Chan, Yanhui Geng:
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models. 5212-5222 - Alexandre Noll Marques, Rémi Lam, Karen Willcox:
Contour location via entropy reduction leveraging multiple information sources. 5223-5233 - Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. 5234-5243 - Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor:
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning. 5244-5253 - Farzan Farnia, David Tse:
A Convex Duality Framework for GANs. 5254-5263 - Alan Malek, Peter L. Bartlett:
Horizon-Independent Minimax Linear Regression. 5264-5273 - Neha Gupta, Aaron Sidford:
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression. 5274-5283 - Erik M. Lindgren, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath:
Experimental Design for Cost-Aware Learning of Causal Graphs. 5284-5294 - Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. 5295-5306 - Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Meta-Reinforcement Learning of Structured Exploration Strategies. 5307-5316 - Tomoya Murata, Taiji Suzuki:
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation. 5317-5326 - Neal Jean, Sang Michael Xie, Stefano Ermon:
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. 5327-5338 - Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese:
Generalizing to Unseen Domains via Adversarial Data Augmentation. 5339-5349 - Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann:
Hyperbolic Neural Networks. 5350-5360 - Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou:
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. 5361-5371 - Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit A. Seshia:
Learning Task Specifications from Demonstrations. 5372-5382 - Anqi Wu, Stan L. Pashkovski, Sandeep R. Datta, Jonathan W. Pillow:
Learning a latent manifold of odor representations from neural responses in piriform cortex. 5383-5393 - Lior Kamma, Casper Benjamin Freksen, Kasper Green Larsen:
Fully Understanding The Hashing Trick. 5394-5404 - Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel:
Evolved Policy Gradients. 5405-5414 - Jeffrey Pennington, Pratik Worah:
The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network. 5415-5424 - John T. Halloran, David M. Rocke:
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra. 5425-5435 - Uri Stemmer, Haim Kaplan:
Differentially Private k-Means with Constant Multiplicative Error. 5436-5446 - Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. 5447-5459 - Weihao Kong, Gregory Valiant:
Estimating Learnability in the Sublinear Data Regime. 5460-5469 - Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh:
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation. 5470-5478 - Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui:
Community Exploration: From Offline Optimization to Online Learning. 5479-5488 - Madhav Nimishakavi, Pratik Kumar Jawanpuria, Bamdev Mishra:
A Dual Framework for Low-rank Tensor Completion. 5489-5500 - Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines:
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames. 5501-5511 - Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu:
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing. 5512-5522 - Shikib Mehri, Leonid Sigal:
Middle-Out Decoding. 5523-5534 - Yi Xu, Rong Jin, Tianbao Yang:
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. 5535-5545 - Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. 5546-5557 - Wonyeol Lee, Hangyeol Yu, Hongseok Yang:
Reparameterization Gradient for Non-differentiable Models. 5558-5568 - Zhize Li, Jian Li:
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization. 5569-5579 - Mike Wu, Noah D. Goodman:
Multimodal Generative Models for Scalable Weakly-Supervised Learning. 5580-5590 - Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi, Javad Lavaei:
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? 5591-5602 - Stuart Armstrong, Sören Mindermann:
Occam's razor is insufficient to infer the preferences of irrational agents. 5603-5614 - Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco:
Manifold Structured Prediction. 5615-5626 - Laming Chen, Guoxin Zhang, Eric Zhou:
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity. 5627-5638 - Yanlin Han, Piotr J. Gmytrasiewicz:
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs. 5639-5647 - Jieming Mao, Renato Paes Leme, Jon Schneider:
Contextual Pricing for Lipschitz Buyers. 5648-5656 - Elad Hazan, Wei Hu, Yuanzhi Li, Zhiyuan Li:
Online Improper Learning with an Approximation Oracle. 5657-5665 - Mario Bravo, David S. Leslie, Panayotis Mertikopoulos:
Bandit Learning in Concave N-Person Games. 5666-5676 - Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco:
On Fast Leverage Score Sampling and Optimal Learning. 5677-5687 - Vikash Goel, Jameson Weng, Pascal Poupart:
Unsupervised Video Object Segmentation for Deep Reinforcement Learning. 5688-5699 - Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos D. Brody, Jonathan W. Pillow:
Efficient inference for time-varying behavior during learning. 5700-5710 - Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch:
Learning convex polytopes with margin. 5711-5721 - Arnu Pretorius, Elan Van Biljon, Steve Kroon, Herman Kamper:
Critical initialisation for deep signal propagation in noisy rectifier neural networks. 5722-5731 - Ari S. Morcos, Maithra Raghu, Samy Bengio:
Insights on representational similarity in neural networks with canonical correlation. 5732-5741 - Dilin Wang, Hao Liu, Qiang Liu:
Variational Inference with Tail-adaptive f-Divergence. 5742-5752 - Jian-Qiao Zhu, Adam Sanborn, Nick Chater:
Mental Sampling in Multimodal Representations. 5753-5764 - Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher:
Adversarially Robust Optimization with Gaussian Processes. 5765-5775 - Yu Zhang, Ying Wei, Qiang Yang:
Learning to Multitask. 5776-5787 - Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho:
Loss Functions for Multiset Prediction. 5788-5797 - Gennaro Auricchio, Federico Bassetti, Stefano Gualandi, Marco Veneroni:
Computing Kantorovich-Wasserstein Distances on d-dimensional histograms using (d+1)-partite graphs. 5798-5808 - Michael Tsang, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, Yan Liu:
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability. 5809-5818 - Liheng Zhang, Marzieh Edraki, Guo-Jun Qi:
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces. 5819-5828 - Trong Dinh Thac Do, Longbing Cao:
Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence. 5829-5840 - Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. 5841-5851 - Eitan Richardson, Yair Weiss:
On GANs and GMMs. 5852-5863 - Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto:
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. 5864-5874 - Stepan Tulyakov, Anton Ivanov, François Fleuret:
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching. 5875-5885 - Toni Karvonen, Chris J. Oates, Simo Särkkä:
A Bayes-Sard Cubature Method. 5886-5897 - Zunlei Feng, Xinchao Wang, Chenglong Ke, Anxiang Zeng, Dacheng Tao, Mingli Song:
Dual Swap Disentangling. 5898-5908 - Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes:
Diverse Ensemble Evolution: Curriculum Data-Model Marriage. 5909-5920 - Takashi Ishida, Gang Niu, Masashi Sugiyama:
Binary Classification from Positive-Confidence Data. 5921-5932 - Michael Tschannen, Eirikur Agustsson, Mario Lucic:
Deep Generative Models for Distribution-Preserving Lossy Compression. 5933-5944 - Alberto Bernacchia, Máté Lengyel, Guillaume Hennequin:
Exact natural gradient in deep linear networks and its application to the nonlinear case. 5945-5954 - Yunho Jeon, Junmo Kim:
Constructing Fast Network through Deconstruction of Convolution. 5955-5965 - Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, Bogdan Raducanu:
Memory Replay GANs: Learning to Generate New Categories without Forgetting. 5966-5976 - Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. 5977-5987 - Gustavo Malkomes, Roman Garnett:
Automating Bayesian optimization with Bayesian optimization. 5988-5997 - Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei (Mark) Zhang:
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning. 5998-6007 - Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. 6008-6018 - Xiaoxi He, Zimu Zhou, Lothar Thiele:
Multi-Task Zipping via Layer-wise Neuron Sharing. 6019-6029 - Oren Mangoubi, Nisheeth K. Vishnoi:
Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo. 6030-6040 - David G. Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas W. Pensyl:
Approximation algorithms for stochastic clustering. 6041-6050 - Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny:
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks. 6051-6061 - Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum:
Learning to Infer Graphics Programs from Hand-Drawn Images. 6062-6071 - Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. 6072-6083 - Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. 6084-6094 - Boyuan Pan, Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He:
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models. 6095-6105 - Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein:
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks. 6106-6116 - Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
Information Constraints on Auto-Encoding Variational Bayes. 6117-6128 - Seungryong Kim, Stephen Lin, Sangryul Jeon, Dongbo Min, Kwanghoon Sohn:
Recurrent Transformer Networks for Semantic Correspondence. 6129-6139 - Jianjun Yuan, Andrew G. Lamperski:
Online convex optimization for cumulative constraints. 6140-6149 - David Madras, Toniann Pitassi, Richard S. Zemel:
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. 6150-6160 - Florian Schmidt, Thomas Hofmann:
Deep State Space Models for Unconditional Word Generation. 6161-6171 - Hongzhou Lin, Stefanie Jegelka:
ResNet with one-neuron hidden layers is a Universal Approximator. 6172-6181 - Andrea Tirinzoni, Rafael Rodríguez-Sánchez, Marcello Restelli:
Transfer of Value Functions via Variational Methods. 6182-6192 - Ian Davidson, Antoine Gourru, S. S. Ravi:
The Cluster Description Problem - Complexity Results, Formulations and Approximations. 6193-6203 - Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. 6204-6213 - Egor Burkov, Victor S. Lempitsky:
Deep Neural Networks with Box Convolutions. 6214-6224 - Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song:
Learning towards Minimum Hyperspherical Energy. 6225-6236 - Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi:
LF-Net: Learning Local Features from Images. 6237-6247 - Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. 6248-6258 - Bart van Merrienboer, Dan Moldovan, Alexander B. Wiltschko:
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming. 6259-6268 - AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang:
Multi-domain Causal Structure Learning in Linear Systems. 6269-6279 - Borja Balle, Gilles Barthe, Marco Gaboardi:
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences. 6280-6290 - Qing Wang, Jiechao Xiong, Lei Han, Peng Sun, Han Liu, Tong Zhang:
Exponentially Weighted Imitation Learning for Batched Historical Data. 6291-6300 - Dennis Leung, Mathias Drton:
Algebraic tests of general Gaussian latent tree models. 6301-6310 - Minjia Zhang, Wenhan Wang, Xiaodong Liu, Jianfeng Gao, Yuxiong He:
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models. 6311-6322 - Colin Graber, Ofer Meshi, Alexander G. Schwing:
Deep Structured Prediction with Nonlinear Output Transformations. 6323-6334 - Emilie Kaufmann, Wouter M. Koolen, Aurélien Garivier:
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling. 6335-6345 - Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. 6346-6357 - Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski:
A no-regret generalization of hierarchical softmax to extreme multi-label classification. 6358-6368 - Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana:
Efficient Formal Safety Analysis of Neural Networks. 6369-6379 - Michael Teng, Frank Wood:
Bayesian Distributed Stochastic Gradient Descent. 6380-6390 - Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. 6391-6401 - Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. 6402-6411 - Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay S. Pande, Jure Leskovec:
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. 6412-6422 - Anirban Laha, Saneem Ahmed Chemmengath, Priyanka Agrawal, Mitesh M. Khapra, Karthik Sankaranarayanan, Harish G. Ramaswamy:
On Controllable Sparse Alternatives to Softmax. 6423-6433 - Michal Rolínek, Georg Martius:
L4: Practical loss-based stepsize adaptation for deep learning. 6434-6444 - Jack Klys, Jake Snell, Richard S. Zemel:
Learning Latent Subspaces in Variational Autoencoders. 6445-6455 - Qiuyuan Huang, Pengchuan Zhang, Dapeng Oliver Wu, Lei Zhang:
Turbo Learning for CaptionBot and DrawingBot. 6456-6466 - Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Jian-Huang Lai, Tie-Yan Liu:
Learning to Teach with Dynamic Loss Functions. 6467-6478 - Edward J. Smith, Scott Fujimoto, David Meger:
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation. 6479-6489 - Bolton Bailey, Matus Telgarsky:
Size-Noise Tradeoffs in Generative Networks. 6490-6500 - Kfir Yehuda Levy, Alp Yurtsever, Volkan Cevher:
Online Adaptive Methods, Universality and Acceleration. 6501-6510 - Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu:
Compact Generalized Non-local Network. 6511-6520 - Huishuai Zhang, Wei Chen, Tie-Yan Liu:
On the Local Hessian in Back-propagation. 6521-6531 - Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. 6532-6541 - Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. 6542-6551 - Shen-Yi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-Jun Li:
Proximal SCOPE for Distributed Sparse Learning. 6552-6561 - Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. 6562-6571 - Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud:
Neural Ordinary Differential Equations. 6572-6583 - Daan Wynen, Cordelia Schmid, Julien Mairal:
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis. 6584-6593 - Asier Mujika, Florian Meier, Angelika Steger:
Approximating Real-Time Recurrent Learning with Random Kronecker Factors. 6594-6603 - Jamie Hayes, Olga Ohrimenko:
Contamination Attacks and Mitigation in Multi-Party Machine Learning. 6604-6616 - Sheng Chen, Arindam Banerjee:
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression. 6617-6628 - Shailee Jain, Alexander Huth:
Incorporating Context into Language Encoding Models for fMRI. 6629-6638 - Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin:
CatBoost: unbiased boosting with categorical features. 6639-6649 - I (Eli) Chien, Chao Pan, Olgica Milenkovic:
Query K-means Clustering and the Double Dixie Cup Problem. 6650-6659 - Zhouyuan Huo, Bin Gu, Heng Huang:
Training Neural Networks Using Features Replay. 6660-6669 - Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang:
Modeling Dynamic Missingness of Implicit Feedback for Recommendation. 6670-6679 - Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun:
Representation Learning of Compositional Data. 6680-6690 - Mikio C. Aoi, Jonathan W. Pillow:
Model-based targeted dimensionality reduction for neuronal population data. 6691-6700 - Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton:
On gradient regularizers for MMD GANs. 6701-6711 - Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez:
Heterogeneous Multi-output Gaussian Process Prediction. 6712-6721 - Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth:
Large-Scale Stochastic Sampling from the Probability Simplex. 6722-6732 - Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri:
Policy Regret in Repeated Games. 6733-6742 - Hendrik Fichtenberger, Dennis Rohde:
A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice. 6743-6754 - Jonas Adler, Sebastian Lunz:
Banach Wasserstein GAN. 6755-6764 - Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang:
Provable Gaussian Embedding with One Observation. 6765-6775 - Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, Yitan Li:
BRITS: Bidirectional Recurrent Imputation for Time Series. 6776-6786 - Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao:
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search. 6787-6798 - Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson:
Extracting Relationships by Multi-Domain Matching. 6799-6810 - Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang:
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses. 6811-6822 - Stanislav Pidhorskyi, Ranya Almohsen, Gianfranco Doretto:
Generative Probabilistic Novelty Detection with Adversarial Autoencoders. 6823-6834 - Maya R. Gupta, Dara Bahri, Andrew Cotter, Kevin Robert Canini:
Diminishing Returns Shape Constraints for Interpretability and Regularization. 6835-6845 - Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau:
Scalable Hyperparameter Transfer Learning. 6846-6856 - Shandian Zhe, Yishuai Du:
Stochastic Nonparametric Event-Tensor Decomposition. 6857-6867 - David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson:
Scaling Gaussian Process Regression with Derivatives. 6868-6878 - Jayadev Acharya, Ziteng Sun, Huanyu Zhang:
Differentially Private Testing of Identity and Closeness of Discrete Distributions. 6879-6891 - Nanyang Ye, Zhanxing Zhu:
Bayesian Adversarial Learning. 6892-6901 - Kishan Wimalawarne, Hiroshi Mamitsuka:
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms. 6902-6910 - Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet:
Maximizing Induced Cardinality Under a Determinantal Point Process. 6911-6920 - Nathan Kallus, Xiaojie Mao, Madeleine Udell:
Causal Inference with Noisy and Missing Covariates via Matrix Factorization. 6921-6932 - Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Steeve Dibangoye:
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions. 6933-6943 - Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias:
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks. 6944-6954 - Stephen Mussmann, Percy Liang:
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. 6955-6964 - Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger:
A Probabilistic U-Net for Segmentation of Ambiguous Images. 6965-6975 - Ming Pang, Wei Gao, Min Tao, Zhi-Hua Zhou:
Unorganized Malicious Attacks Detection. 6976-6985 - Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
Causal Inference via Kernel Deviance Measures. 6986-6994 - Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. 6995-7004 - Yingyezhe Jin, Wenrui Zhang, Peng Li:
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks. 7005-7015 - Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. 7016-7025 - Pierre Gaillard, Olivier Wintenberger:
Efficient online algorithms for fast-rate regret bounds under sparsity. 7026-7036 - Alexander A. Alemi, Ian Fischer:
GILBO: One Metric to Measure Them All. 7037-7046 - Andrey Malinin, Mark J. F. Gales:
Predictive Uncertainty Estimation via Prior Networks. 7047-7058 - Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell:
Dual Policy Iteration. 7059-7069 - Sabyasachi Shivkumar, Richard D. Lange, Ankani Chattoraj, Ralf M. Haefner:
A probabilistic population code based on neural samples. 7070-7079 - Anirvan M. Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri B. Chklovskii:
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks. 7080-7090 - Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee:
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport. 7091-7101 - Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar:
Model-Agnostic Private Learning. 7102-7112 - Tengfei Ma, Jie Chen, Cao Xiao:
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders. 7113-7124 - Sham M. Kakade, Jason D. Lee:
Provably Correct Automatic Sub-Differentiation for Qualified Programs. 7125-7135 - Priyank Jaini, Pascal Poupart, Yaoliang Yu:
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. 7136-7145 - Grant M. Rotskoff, Eric Vanden-Eijnden:
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. 7146-7155 - Sungryull Sohn, Junhyuk Oh, Honglak Lee:
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. 7156-7166 - Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin:
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. 7167-7177 - Filipe de Avila Belbute-Peres, Kevin A. Smith, Kelsey R. Allen, Josh Tenenbaum, J. Zico Kolter:
End-to-End Differentiable Physics for Learning and Control. 7178-7189 - Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre:
BRUNO: A Deep Recurrent Model for Exchangeable Data. 7190-7198 - Fabian H. Sinz, Alexander S. Ecker, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Emmanouil Froudarakis, Dimitri Yatsenko, Zachary Pitkow, Jacob Reimer, Andreas S. Tolias:
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video. 7199-7210 - Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson:
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. 7211-7221 - Ilai Bistritz, Amir Leshem:
Distributed Multi-Player Bandits - a Game of Thrones Approach. 7222-7232 - Itay Evron, Edward Moroshko, Koby Crammer:
Efficient Loss-Based Decoding on Graphs for Extreme Classification. 7233-7244 - Amir-Reza Asadi, Emmanuel Abbe, Sergio Verdú:
Chaining Mutual Information and Tightening Generalization Bounds. 7245-7254 - Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead:
Implicit Probabilistic Integrators for ODEs. 7255-7264 - Jiechuan Jiang, Zongqing Lu:
Learning Attentional Communication for Multi-Agent Cooperation. 7265-7275 - Tyler B. Johnson, Carlos Guestrin:
Training Deep Models Faster with Robust, Approximate Importance Sampling. 7276-7286 - Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun:
Bandit Learning with Implicit Feedback. 7287-7297 - Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick:
Unsupervised Text Style Transfer using Language Models as Discriminators. 7298-7309 - Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap:
Relational recurrent neural networks. 7310-7321 - Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora:
Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features. 7322-7332 - Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Y. Chang:
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis. 7333-7342 - Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn:
Bayesian Model-Agnostic Meta-Learning. 7343-7353 - Mahyar Khayatkhoei, Maneesh Singh, Ahmed Elgammal:
Disconnected Manifold Learning for Generative Adversarial Networks. 7354-7364 - Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James R. Glass:
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces. 7365-7375 - Victor-Emmanuel Brunel:
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem. 7376-7385 - Gabi Shalev, Yossi Adi, Joseph Keshet:
Out-of-Distribution Detection using Multiple Semantic Label Representations. 7386-7396 - Insu Han, Haim Avron, Jinwoo Shin:
Stochastic Chebyshev Gradient Descent for Spectral Optimization. 7397-7407 - Sofiane Dhouib, Ievgen Redko:
Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation. 7408-7417 - Marina Meila:
How to tell when a clustering is (approximately) correct using convex relaxations. 7418-7429 - Zakaria Mhammedi, Robert C. Williamson:
Constant Regret, Generalized Mixability, and Mirror Descent. 7430-7439 - Wonseok Jeon, Seokin Seo, Kee-Eung Kim:
A Bayesian Approach to Generative Adversarial Imitation Learning. 7440-7450 - Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, Philip Schniter:
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis. 7451-7460 - Min Wen, Ufuk Topcu:
Constrained Cross-Entropy Method for Safe Reinforcement Learning. 7461-7471 - Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon:
Multi-Agent Generative Adversarial Imitation Learning. 7472-7483 - Yonatan Gur, Ahmadreza Momeni:
Adaptive Learning with Unknown Information Flows. 7484-7493 - Bryan Lim:
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. 7494-7504 - Namrata Anand, Po-Ssu Huang:
Generative modeling for protein structures. 7505-7516 - Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes:
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. 7517-7527 - Xu Lan, Xiatian Zhu, Shaogang Gong:
Knowledge Distillation by On-the-Fly Native Ensemble. 7528-7538 - Yedid Hoshen:
Non-Adversarial Mapping with VAEs. 7539-7548 - Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann:
Generalisation in humans and deep neural networks. 7549-7561 - Sandeep Subramanian, Sai Rajeswar, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal:
Towards Text Generation with Adversarially Learned Neural Outlines. 7562-7574 - Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. 7575-7586 - Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson:
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. 7587-7597 - Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin:
Diffusion Maps for Textual Network Embedding. 7598-7608 - Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul:
Simple, Distributed, and Accelerated Probabilistic Programming. 7609-7620 - Kevin Duarte, Yogesh S. Rawat, Mubarak Shah:
VideoCapsuleNet: A Simplified Network for Action Detection. 7621-7630 - Xuhui Fan, Bin Li, Scott A. Sisson:
Rectangular Bounding Process. 7631-7641 - Huy L. Nguyen, Lydia Zakynthinou:
Improved Algorithms for Collaborative PAC Learning. 7642-7650 - Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio:
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. 7651-7662 - Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu:
Communication Compression for Decentralized Training. 7663-7673 - Noam Brown, Tuomas Sandholm, Brandon Amos:
Depth-Limited Solving for Imperfect-Information Games. 7674-7685 - Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan:
Training Deep Neural Networks with 8-bit Floating Point Numbers. 7686-7695 - Georgios Theocharous, Zheng Wen, Yasin Abbasi, Nikos Vlassis:
Scalar Posterior Sampling with Applications. 7696-7704 - Johan Bjorck, Carla P. Gomes, Bart Selman, Kilian Q. Weinberger:
Understanding Batch Normalization. 7705-7716 - Rakshith Shetty, Mario Fritz, Bernt Schiele:
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision. 7717-7727 - Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang:
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples. 7728-7739 - Pierre Baldi, Roman Vershynin:
On Neuronal Capacity. 7740-7749 - Sebastian Flennerhag, Hujun Yin, John A. Keane, Mark J. Elliot:
Breaking the Activation Function Bottleneck through Adaptive Parameterization. 7750-7761 - Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song:
Learning Loop Invariants for Program Verification. 7762-7773 - Bruno Korbar, Du Tran, Lorenzo Torresani:
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization. 7774-7785 - David Alvarez-Melis, Tommi S. Jaakkola:
Towards Robust Interpretability with Self-Explaining Neural Networks. 7786-7795 - Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski:
Deep State Space Models for Time Series Forecasting. 7796-7805 - Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Constrained Graph Variational Autoencoders for Molecule Design. 7806-7815 - Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum:
Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction. 7816-7826 - Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu:
Neural Architecture Optimization. 7827-7838 - Yao-Xiang Ding, Zhi-Hua Zhou:
Preference Based Adaptation for Learning Objectives. 7839-7848 - Shi Li, Xiangyu Guo:
Distributed k-Clustering for Data with Heavy Noise. 7849-7857 - Holden Lee, Andrej Risteski, Rong Ge:
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. 7858-7867 - Joseph Marino, Milan Cvitkovic, Yisong Yue:
A General Method for Amortizing Variational Filtering. 7868-7879 - Matteo Almanza, Flavio Chierichetti, Alessandro Panconesi, Andrea Vattani:
A Reduction for Efficient LDA Topic Reconstruction. 7880-7890 - Dominik Linzner, Heinz Koeppl:
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. 7891-7901 - Thu Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yong-Liang Yang:
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes. 7902-7912 - Rui Gao, Liyan Xie, Yao Xie, Huan Xu:
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets. 7913-7923 - Zhihao Zheng, Pengyu Hong:
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks. 7924-7933 - Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim:
Monte-Carlo Tree Search for Constrained POMDPs. 7934-7943 - Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin:
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. 7944-7954 - Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu:
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation. 7955-7965 - He Zhao, Lan Du, Wray L. Buntine, Mingyuan Zhou:
Dirichlet belief networks for topic structure learning. 7966-7977 - Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang:
Stochastic Expectation Maximization with Variance Reduction. 7978-7988 - Wenruo Bai, William Stafford Noble, Jeff A. Bilmes:
Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions. 7989-7999 - Sander Dieleman, Aäron van den Oord, Karen Simonyan:
The challenge of realistic music generation: modelling raw audio at scale. 8000-8010 - Brandon Tran, Jerry Li, Aleksander Madry:
Spectral Signatures in Backdoor Attacks. 8011-8021 - Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei:
Reward learning from human preferences and demonstrations in Atari. 8022-8034 - Tal Friedman, Guy Van den Broeck:
Approximate Knowledge Compilation by Online Collapsed Importance Sampling. 8035-8045 - Andrew Trask, Felix Hill, Scott E. Reed, Jack W. Rae, Chris Dyer, Phil Blunsom:
Neural Arithmetic Logic Units. 8046-8055 - Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander G. Schwing:
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training. 8056-8067 - Xundong Wu, Xiangwen Liu, Wei Li, Qing Wu:
Improved Expressivity Through Dendritic Neural Networks. 8068-8079 - Vatsal Sharan, Parikshit Gopalan, Udi Wieder:
Efficient Anomaly Detection via Matrix Sketching. 8080-8091 - Jonghwan Mun, Kimin Lee, Jinwoo Shin, Bohyung Han:
Learning to Specialize with Knowledge Distillation for Visual Question Answering. 8092-8102 - Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. 8103-8112 - Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Credit Assignment For Collective Multiagent RL With Global Rewards. 8113-8124 - Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. 8125-8135 - Zachary C. Lipton, Julian J. McAuley, Alexandra Chouldechova:
Does mitigating ML's impact disparity require treatment disparity? 8136-8146 - Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao:
Proximal Graphical Event Models. 8147-8156 - Mahdi Imani, Seyede Fatemeh Ghoreishi, Ulisses M. Braga-Neto:
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments. 8157-8167 - Yuanzhi Li, Yingyu Liang:
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data. 8168-8177 - Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. 8178-8188 - James Thewlis, Hakan Bilen, Andrea Vedaldi:
Modelling and unsupervised learning of symmetric deformable object categories. 8189-8200 - Fredrik Lindsten, Jouni Helske, Matti Vihola:
Graphical model inference: Sequential Monte Carlo meets deterministic approximations. 8201-8211 - Jonathan Kadmon, Surya Ganguli:
Statistical mechanics of low-rank tensor decomposition. 8212-8222 - Luigi Acerbi:
Variational Bayesian Monte Carlo. 8223-8233 - Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee:
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. 8234-8244 - Haipeng Luo, Chen-Yu Wei, Kai Zheng:
Efficient Online Portfolio with Logarithmic Regret. 8245-8255 - Judy Hoffman, Mehryar Mohri, Ningshan Zhang:
Algorithms and Theory for Multiple-Source Adaptation. 8256-8266 - Claudio Gentile, Nikos Parotsidis, Fabio Vitale:
Online Reciprocal Recommendation with Theoretical Performance Guarantees. 8267-8277 - Nicolas Brosse, Alain Durmus, Eric Moulines:
The promises and pitfalls of Stochastic Gradient Langevin Dynamics. 8278-8288 - Lei Wu, Chao Ma, Weinan E:
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective. 8289-8298 - Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter:
Differentiable MPC for End-to-end Planning and Control. 8299-8310 - Jordan Frécon, Saverio Salzo, Massimiliano Pontil:
Bilevel learning of the Group Lasso structure. 8311-8321 - Yang Song, Rui Shu, Nate Kushman, Stefano Ermon:
Constructing Unrestricted Adversarial Examples with Generative Models. 8322-8333 - Chuyang Ke, Jean Honorio:
Information-theoretic Limits for Community Detection in Network Models. 8334-8343 - Will Norcliffe-Brown, Stathis Vafeias, Sarah Parisot:
Learning Conditioned Graph Structures for Interpretable Visual Question Answering. 8344-8353 - Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Graphical Models. 8354-8365 - Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo:
Transfer Learning with Neural AutoML. 8366-8375 - Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu:
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity. 8376-8385 - Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro:
On preserving non-discrimination when combining expert advice. 8386-8397 - Nick Haber, Damian Mrowca, Stephanie Wang, Li Fei-Fei, Daniel L. K. Yamins:
Learning to Play With Intrinsically-Motivated, Self-Aware Agents. 8398-8409 - Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter:
Scaling provable adversarial defenses. 8410-8419 - Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu:
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images. 8420-8429 - Han Shao, Xiaotian Yu, Irwin King, Michael R. Lyu:
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs. 8430-8439 - Gintare Karolina Dziugaite, Daniel M. Roy:
Data-dependent PAC-Bayes priors via differential privacy. 8440-8450 - Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou:
Deep Poisson gamma dynamical systems. 8451-8461 - Kry Yik Chau Lui, Gavin Weiguang Ding, Ruitong Huang, Robert J. McCann:
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds. 8462-8472 - Luis Haug, Sebastian Tschiatschek, Adish Singla:
Teaching Inverse Reinforcement Learners via Features and Demonstrations. 8473-8482 - Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani:
Wasserstein Distributionally Robust Kalman Filtering. 8483-8492 - James C. R. Whittington, Timothy H. Muller, Shirely Mark, Caswell Barry, Tim E. J. Behrens:
Generalisation of structural knowledge in the hippocampal-entorhinal system. 8493-8504 - Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro:
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. 8505-8515 - Sebastian Lunz, Carola Schönlieb, Ozan Öktem:
Adversarial Regularizers in Inverse Problems. 8516-8525 - Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn:
Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms. 8526-8535 - Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-teaching: Robust training of deep neural networks with extremely noisy labels. 8536-8546 - Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine:
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. 8547-8556 - Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi:
A convex program for bilinear inversion of sparse vectors. 8557-8567 - Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, João Paulo Costeira, Geoffrey J. Gordon:
Adversarial Multiple Source Domain Adaptation. 8568-8579 - Arthur Jacot, Clément Hongler, Franck Gabriel:
Neural Tangent Kernel: Convergence and Generalization in Neural Networks. 8580-8589 - Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel:
Contextual Stochastic Block Models. 8590-8602 - Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song:
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks. 8603-8614 - Adam R. Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner:
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects. 8615-8625 - Ian Osband, John Aslanides, Albin Cassirer:
Randomized Prior Functions for Deep Reinforcement Learning. 8626-8638 - Craig S. Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum:
Compact Representation of Uncertainty in Clustering. 8639-8649 - Fariborz Salehi, Ehsan Abbasi, Babak Hassibi:
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity. 8655-8666 - Michelle Yuan, Benjamin Van Durme, Jordan L. Ying:
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. 8667-8677 - Arman Rahimzamani, Himanshu Asnani, Pramod Viswanath, Sreeram Kannan:
Estimators for Multivariate Information Measures in General Probability Spaces. 8678-8689 - Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble:
DeepPINK: reproducible feature selection in deep neural networks. 8690-8700 - Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri:
HOUDINI: Lifelong Learning as Program Synthesis. 8701-8712 - Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens:
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction. 8713-8724 - Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. 8725-8734 - João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn:
Dendritic cortical microcircuits approximate the backpropagation algorithm. 8735-8746 - Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. 8747-8758 - Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. 8759-8770 - Bart van Merrienboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin:
Automatic differentiation in ML: Where we are and where we should be going. 8771-8781 - Diana Cai, Michael Mitzenmacher, Ryan P. Adams:
A Bayesian Nonparametric View on Count-Min Sketch. 8782-8791 - Zhilu Zhang, Mert R. Sabuncu:
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. 8792-8802 - Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. 8803-8812 - Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Josh Tenenbaum, Daniel L. K. Yamins:
Flexible neural representation for physics prediction. 8813-8824 - Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda:
Legendre Decomposition for Tensors. 8825-8835 - Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Miroslav Olsák:
Reinforcement Learning of Theorem Proving. 8836-8847 - Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu:
Data Amplification: A Unified and Competitive Approach to Property Estimation. 8848-8857 - Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski:
Group Equivariant Capsule Networks. 8858-8867 - Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent as Moment Matching. 8868-8877 - Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tao Tantipongpipat:
Differential Privacy for Growing Databases. 8878-8887 - Jungseul Ok, Alexandre Proutière, Damianos Tranos:
Exploration in Structured Reinforcement Learning. 8888-8896 - Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek B. Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri:
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices. 8897-8908 - Nathan Kallus:
Balanced Policy Evaluation and Learning. 8909-8920 - Rasul Tutunov, Dongho Kim, Haitham Bou-Ammar:
Distributed Multitask Reinforcement Learning with Quadratic Convergence. 8921-8930 - Richard Shin, Illia Polosukhin, Dawn Song:
Improving Neural Program Synthesis with Inferred Execution Traces. 8931-8940 - Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi:
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems. 8941-8952 - Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian D. Ziebart:
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. 8953-8963 - Zhilin Yang, Junbo Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun:
GLoMo: Unsupervised Learning of Transferable Relational Graphs. 8964-8975 - Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak:
Online Learning of Quantum States. 8976-8986 - Asad Haris, Ali Shojaie, Noah Simon:
Wavelet regression and additive models for irregularly spaced data. 8987-8997 - Rico Angell, Daniel Sheldon:
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes. 8998-9007 - Anna Korba, Alexandre Garcia, Florence d'Alché-Buc:
A Structured Prediction Approach for Label Ranking. 9008-9018 - Mojmir Mutny, Andreas Krause:
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features. 9019-9030 - Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma:
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. 9031-9042 - Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse:
Reversible Recurrent Neural Networks. 9043-9054 - Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, Francis R. Bach:
SING: Symbol-to-Instrument Neural Generator. 9055-9065 - Anna T. Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Ré:
Learning Compressed Transforms with Low Displacement Rank. 9066-9078 - Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin:
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. 9079-9089 - Amir-massoud Farahmand:
Iterative Value-Aware Model Learning. 9090-9101 - Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg:
Invariant Representations without Adversarial Training. 9102-9111 - Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi, Lerrel Pinto:
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias. 9112-9122 - Jessie Huang, Fa Wu, Doina Precup, Yang Cai:
Learning Safe Policies with Expert Guidance. 9123-9132 - Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian:
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. 9133-9142 - Vikas K. Garg, Ofer Dekel, Lin Xiao:
Learning SMaLL Predictors. 9143-9153 - Paul Hand, Oscar Leong, Vladislav Voroninski:
Phase Retrieval Under a Generative Prior. 9154-9164 - Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan V. Oseledets:
Quadrature-based features for kernel approximation. 9165-9174 - Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult:
Reducing Network Agnostophobia. 9175-9186 - Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl:
A Stein variational Newton method. 9187-9197 - Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi:
Watch Your Step: Learning Node Embeddings via Graph Attention. 9198-9208 - Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine:
Visual Reinforcement Learning with Imagined Goals. 9209-9220 - Kuan Han, Haiguang Wen, Yizhen Zhang, Di Fu, Eugenio Culurciello, Zhongming Liu:
Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition. 9221-9233 - Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun:
PAC-Bayes bounds for stable algorithms with instance-dependent priors. 9234-9244 - Yin Li, Abhinav Gupta:
Beyond Grids: Learning Graph Representations for Visual Recognition. 9245-9255 - Constantinos Daskalakis, Ioannis Panageas:
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization. 9256-9266 - Farnood Salehi, Patrick Thiran, L. Elisa Celis:
Coordinate Descent with Bandit Sampling. 9267-9277 - Jeremy Morton, Antony Jameson, Mykel J. Kochenderfer, Freddie D. Witherden:
Deep Dynamical Modeling and Control of Unsteady Fluid Flows. 9278-9288 - Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Improvement. 9289-9299 - Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever:
The Importance of Sampling inMeta-Reinforcement Learning. 9300-9310 - Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. 9311-9321 - Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris:
The Effect of Network Width on the Performance of Large-batch Training. 9322-9332 - Bharat Singh, Mahyar Najibi, Larry S. Davis:
SNIPER: Efficient Multi-Scale Training. 9333-9343 - Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. 9344-9354 - Tao Chen, Adithyavairavan Murali, Abhinav Gupta:
Hardware Conditioned Policies for Multi-Robot Transfer Learning. 9355-9366 - Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, Bill Freeman, Gregory W. Wornell:
Co-regularized Alignment for Unsupervised Domain Adaptation. 9367-9378 - Daniele Calandriello, Lorenzo Rosasco:
Statistical and Computational Trade-Offs in Kernel K-Means. 9379-9389 - Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. 9390-9400 - Yan Wu, Gregory Wayne, Karol Gregor, Timothy P. Lillicrap:
Learning Attractor Dynamics for Generative Memory. 9401-9410 - Samuel A. Ocko, Jack Lindsey, Surya Ganguli, Stéphane Deny:
The emergence of multiple retinal cell types through efficient coding of natural movies. 9411-9422 - Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi:
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. 9423-9433 - Zi Yin, Vin Sachidananda, Balaji Prabhakar:
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation. 9434-9445 - Eli Sherman, Ilya Shpitser:
Identification and Estimation of Causal Effects from Dependent Data. 9446-9457 - Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath:
Deepcode: Feedback Codes via Deep Learning. 9458-9468 - Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy:
Learning and Testing Causal Models with Interventions. 9469-9481 - Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro:
Implicit Bias of Gradient Descent on Linear Convolutional Networks. 9482-9491 - Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Continuous Optimization for Structure Learning. 9492-9503 - Tin D. Nguyen, Samory Kpotufe:
PAC-Bayes Tree: weighted subtrees with guarantees. 9504-9512 - Rajan Udwani:
Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint. 9513-9524 - Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. 9525-9536 - Chelsea Finn, Kelvin Xu, Sergey Levine:
Probabilistic Model-Agnostic Meta-Learning. 9537-9548 - Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach. 9549-9559 - Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom:
e-SNLI: Natural Language Inference with Natural Language Explanations. 9560-9572 - Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent:
Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis. 9573-9583 - Jack Umenberger, Thomas B. Schön:
Learning convex bounds for linear quadratic control policy synthesis. 9584-9595 - Morteza Mardani, Qingyun Sun, David L. Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John M. Pauly:
Neural Proximal Gradient Descent for Compressive Imaging. 9596-9606 - Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John E. Hopcroft:
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. 9607-9616 - Rad Niazadeh, Tim Roughgarden, Joshua R. Wang:
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization. 9617-9627 - Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski:
An intriguing failing of convolutional neural networks and the CoordConv solution. 9628-9639 - Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos:
Trading robust representations for sample complexity through self-supervised visual experience. 9640-9650 - Fangchang Ma, Ulas Ayaz, Sertac Karaman:
Invertibility of Convolutional Generative Networks from Partial Measurements. 9651-9660 - Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm:
Ex ante coordination and collusion in zero-sum multi-player extensive-form games. 9661-9671 - Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong:
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization. 9672-9683 - Manish Purohit, Zoya Svitkina, Ravi Kumar:
Improving Online Algorithms via ML Predictions. 9684-9693 - Murat A. Erdogdu, Lester Mackey, Ohad Shamir:
Global Non-convex Optimization with Discretized Diffusions. 9694-9703 - Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan:
Theoretical guarantees for EM under misspecified Gaussian mixture models. 9704-9712 - Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song:
Coupled Variational Bayes via Optimization Embedding. 9713-9723 - Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron C. Courville:
Improving Explorability in Variational Inference with Annealed Variational Objectives. 9724-9734 - Yuntian Deng, Yoon Kim, Justin T. Chiu, Demi Guo, Alexander M. Rush:
Latent Alignment and Variational Attention. 9735-9747 - Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal:
Towards Deep Conversational Recommendations. 9748-9758 - Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Chris Pal:
Unsupervised Depth Estimation, 3D Face Rotation and Replacement. 9759-9769 - Vitaly Feldman, Jan Vondrák:
Generalization Bounds for Uniformly Stable Algorithms. 9770-9780 - Izhak Golan, Ran El-Yaniv:
Deep Anomaly Detection Using Geometric Transformations. 9781-9791 - Théo Lacombe, Marco Cuturi, Steve Oudot:
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport. 9792-9802 - Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu:
Entropy Rate Estimation for Markov Chains with Large State Space. 9803-9814 - Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar:
Adaptive Methods for Nonconvex Optimization. 9815-9825 - Guangxiang Zhu, Zhiao Huang, Chongjie Zhang:
Object-Oriented Dynamics Predictor. 9826-9837 - Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. 9838-9848 - Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi:
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. 9849-9860 - MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác:
Reinforcement Learning for Solving the Vehicle Routing Problem. 9861-9871 - Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. 9872-9883 - Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec:
Dynamic Network Model from Partial Observations. 9884-9894 - Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins:
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. 9895-9905 - James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. 9906-9917 - Tao Sun, Yuejiao Sun, Wotao Yin:
On Markov Chain Gradient Descent. 9918-9927 - Ellango Jothimurugesan, Ashraf Tahmasbi, Phillip B. Gibbons, Srikanta Tirthapura:
Variance-Reduced Stochastic Gradient Descent on Streaming Data. 9928-9937 - Aaron J. Havens, Zhanhong Jiang, Soumik Sarkar:
Online Robust Policy Learning in the Presence of Unknown Adversaries. 9938-9948 - Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama:
Uplift Modeling from Separate Labels. 9949-9959 - Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. 9960-9970 - Tyler Lu, Dale Schuurmans, Craig Boutilier:
Non-delusional Q-learning and value-iteration. 9971-9981 - Tomas Geffner, Justin Domke:
Using Large Ensembles of Control Variates for Variational Inference. 9982-9992 - Yuanxiang Gao, Li Chen, Baochun Li:
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization. 9993-10002 - Imanol Schlag, Jürgen Schmidhuber:
Learning to Reason with Third Order Tensor Products. 10003-10014 - Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao:
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing. 10015-10027 - Tam Le, Makoto Yamada:
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams. 10028-10039 - Sercan Ömer Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou:
Neural Voice Cloning with a Few Samples. 10040-10050 - Ali Ahmed, Alireza Aghasi, Paul Hand:
Blind Deconvolutional Phase Retrieval via Convex Programming. 10051-10061 - Imtiaz Masud Ziko, Eric Granger, Ismail Ben Ayed:
Scalable Laplacian K-modes. 10062-10072 - Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang:
A Retrieve-and-Edit Framework for Predicting Structured Outputs. 10073-10083 - Alistair Stewart, Ilias Diakonikolas, Clément L. Canonne:
Testing for Families of Distributions via the Fourier Transform. 10084-10095 - Mitali Bafna, Jack Murtagh, Nikhil Vyas:
Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform. 10096-10106 - Mitchell Stern, Noam Shazeer, Jakob Uszkoreit:
Blockwise Parallel Decoding for Deep Autoregressive Models. 10107-10116 - Osman Asif Malik, Stephen Becker:
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch. 10117-10127 - A. Emin Orhan:
A Simple Cache Model for Image Recognition. 10128-10137 - Risi Kondor, Zhen Lin, Shubhendu Trivedi:
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. 10138-10147 - Felipe A. Tobar:
Bayesian Nonparametric Spectral Estimation. 10148-10158 - Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant:
A Spectral View of Adversarially Robust Features. 10159-10169 - Chen Lin, Zhao Zhong, Wei Wu, Junjie Yan:
Synaptic Strength For Convolutional Neural Network. 10170-10179 - Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez:
Human-in-the-Loop Interpretability Prior. 10180-10189 - Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil:
Learning To Learn Around A Common Mean. 10190-10200 - Fei Wang, James M. Decker, Xilun Wu, Grégory M. Essertel, Tiark Rompf:
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming. 10201-10212 - Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco:
Learning with SGD and Random Features. 10213-10224 - Paavo Parmas:
Total stochastic gradient algorithms and applications in reinforcement learning. 10225-10235 - Diederik P. Kingma, Prafulla Dhariwal:
Glow: Generative Flow with Invertible 1x1 Convolutions. 10236-10245 - Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos:
Nonparametric Density Estimation under Adversarial Losses. 10246-10257 - Boris Muzellec, Marco Cuturi:
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions. 10258-10269 - Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab:
Learning to Share and Hide Intentions using Information Regularization. 10270-10281 - Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He:
Predictive Approximate Bayesian Computation via Saddle Points. 10282-10291 - Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh:
Robustness of conditional GANs to noisy labels. 10292-10303 - Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart:
Robust Learning of Fixed-Structure Bayesian Networks. 10304-10316 - Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen:
Improving Simple Models with Confidence Profiles. 10317-10327 - Joseph M. Antognini, Jascha Sohl-Dickstein:
PCA of high dimensional random walks with comparison to neural network training. 10328-10337 - Mislav Balunovic, Pavol Bielik, Martin T. Vechev:
Learning to Solve SMT Formulas. 10338-10349 - Nicholas Gallo, Alexander Ihler:
Lifted Weighted Mini-Bucket. 10350-10358 - Siddarth Srinivasan, Carlton Downey, Byron Boots:
Learning and Inference in Hilbert Space with Quantum Graphical Models. 10359-10368 - Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi:
Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound. 10369-10379 - Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution. 10380-10389 - Francesco Paolo Casale, Adrian V. Dalca, Luca Saglietti, Jennifer Listgarten, Nicoló Fusi:
Gaussian Process Prior Variational Autoencoders. 10390-10401 - Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. 10402-10413 - Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz:
Context-aware Synthesis and Placement of Object Instances. 10414-10424 - Jessica Finocchiaro, Rafael M. Frongillo:
Convex Elicitation of Continuous Properties. 10425-10434 - Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman:
Mesh-TensorFlow: Deep Learning for Supercomputers. 10435-10444 - Matthew Riemer, Miao Liu, Gerald Tesauro:
Learning Abstract Options. 10445-10455 - Rafael M. Frongillo, Bo Waggoner:
Bounded-Loss Private Prediction Markets. 10456-10465 - Lea Duncker, Maneesh Sahani:
Temporal alignment and latent Gaussian process factor inference in population spike trains. 10466-10476 - Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel:
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. 10477-10486 - Trefor W. Evans, Prasanth B. Nair:
Discretely Relaxing Continuous Variables for tractable Variational Inference. 10487-10497 - Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling:
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior. 10498-10509 - Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee:
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. 10510-10521 - Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Lianhui Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing:
Deep Generative Models with Learnable Knowledge Constraints. 10522-10533 - Yubei Chen, Dylan M. Paiton, Bruno A. Olshausen:
The Sparse Manifold Transform. 10534-10545 - Raanan Y. Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik:
Bayesian Structure Learning by Recursive Bootstrap. 10546-10556 - Moritz Wolter, Angela Yao:
Complex Gated Recurrent Neural Networks. 10557-10567 - Abubakar Abid, James Y. Zou:
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders. 10568-10578 - Aditya Grover, Tudor Achim, Stefano Ermon:
Streamlining Variational Inference for Constraint Satisfaction Problems. 10579-10589 - Steven Hansen, Alexander Pritzel, Pablo Sprechmann, André Barreto, Charles Blundell:
Fast deep reinforcement learning using online adjustments from the past. 10590-10600 - Alexander Matyasko, Lap-Pui Chau:
Improved Network Robustness with Adversary Critic. 10601-10610 - Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint. 10611-10620 - Wei Sun, Junwei Lu, Han Liu:
Sketching Method for Large Scale Combinatorial Inference. 10621-10630 - Arun Sai Suggala, Adarsh Prasad, Pradeep Ravikumar:
Connecting Optimization and Regularization Paths. 10631-10641 - Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung:
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices. 10642-10653 - Yilin Zhang, Karl Rohe:
Understanding Regularized Spectral Clustering via Graph Conductance. 10654-10663 - Maria-Florina Balcan, Travis Dick, Colin White:
Data-Driven Clustering via Parameterized Lloyd's Families. 10664-10674 - Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon:
Learning Beam Search Policies via Imitation Learning. 10675-10684 - Ji Xu, Daniel J. Hsu, Arian Maleki:
Benefits of over-parameterization with EM. 10685-10695 - Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu:
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning. 10696-10705 - Roie Levin, Anish Prasad Sevekari, David P. Woodruff:
Robust Subspace Approximation in a Stream. 10706-10716 - Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan:
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues. 10717-10727 - Yair Carmon, John C. Duchi:
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. 10728-10738 - Matthew D. Hoffman:
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. 10739-10749 - Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le:
DropBlock: A regularization method for convolutional networks. 10750-10760 - Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Daniel Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier:
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger. 10761-10771 - Saumya Jetley, Nicholas A. Lord, Philip H. S. Torr:
With Friends Like These, Who Needs Adversaries? 10772-10782 - Pavel E. Dvurechenskii, Darina Dvinskikh, Alexander V. Gasnikov, César A. Uribe, Angelia Nedich:
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters. 10783-10793 - David Minnen, Johannes Ballé, George Toderici:
Joint Autoregressive and Hierarchical Priors for Learned Image Compression. 10794-10803 - Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song:
Learning Temporal Point Processes via Reinforcement Learning. 10804-10814 - Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon:
Bias and Generalization in Deep Generative Models: An Empirical Study. 10815-10824 - Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev:
Fast and Effective Robustness Certification. 10825-10836 - Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli:
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. 10837-10847 - Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang:
Differentially Private Change-Point Detection. 10848-10857 - Tong Wang:
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations. 10858-10868 - Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij:
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. 10869-10879 - Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos H. Papadimitriou, Amin Saberi, Santosh S. Vempala:
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons. 10880-10890 - Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin:
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. 10891-10899 - Aditi Raghunathan, Jacob Steinhardt, Percy Liang:
Semidefinite relaxations for certifying robustness to adversarial examples. 10900-10910 - Nathan Kallus, Aahlad Manas Puli, Uri Shalit:
Removing Hidden Confounding by Experimental Grounding. 10911-10920 - Ankush Mandal, He Jiang, Anshumali Shrivastava, Vivek Sarkar:
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements. 10921-10931 - Yi Chen, Zhuoran Yang, Yuchen Xie, Zhaoran Wang:
Contrastive Learning from Pairwise Measurements. 10932-10941 - Anuj Sharma, Robert Johnson, Florian Engert, Scott W. Linderman:
Point process latent variable models of larval zebrafish behavior. 10942-10953 - Kevin Bello, Jean Honorio:
Computationally and statistically efficient learning of causal Bayes nets using path queries. 10954-10964 - Guy Bresler, Sung Min Park, Madalina Persu:
Sparse PCA from Sparse Linear Regression. 10965-10975 - Don Kurian Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain:
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. 10976-10987 - Aniket (Nick) Bajpai, Sankalp Garg, Mausam:
Transfer of Deep Reactive Policies for MDP Planning. 10988-10998 - Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala:
The Price of Fair PCA: One Extra dimension. 10999-11010 - Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh:
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking. 11011-11021
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-09-17 00:20 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
