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ORCID27th ICML 2010: Haifa, Israel
- Johannes Fürnkranz, Thorsten Joachims:
Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel. Omnipress 2010 - Chid Apté:
The Role of Machine Learning in Business Optimization. 1-2 - Mark Joseph Cummins, Paul M. Newman:
FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model. 3-10 - Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan:
Discriminative Latent Variable Models for Object Detection. 11-12 - Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich:
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. 13-20 - Christopher Raphael:
Music Plus One and Machine Learning. 21-28 - Benjamin Snyder, Regina Barzilay:
Climbing the Tower of Babel: Unsupervised Multilingual Learning. 29-36 - Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan:
Detecting Large-Scale System Problems by Mining Console Logs. 37-46 - Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth:
Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. 47-54 - Rémi Bardenet, Balázs Kégl:
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. 55-62 - Nicholas Bartlett, David Pfau, Frank D. Wood:
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. 63-70 - Sahely Bhadra, Sourangshu Bhattacharya
, Chiranjib Bhattacharyya, Aharon Ben-Tal:
Robust Formulations for Handling Uncertainty in Kernel Matrices. 71-78 - Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor:
Active Learning for Networked Data. 79-86 - David M. Blei, Peter I. Frazier:
Distance dependent Chinese restaurant processes. 87-94 - Gianluca Bontempi, Patrick E. Meyer:
Causal filter selection in microarray data. 95-102 - Antoine Bordes, Nicolas Usunier, Jason Weston:
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences. 103-110 - Y-Lan Boureau, Jean Ponce, Yann LeCun:
A Theoretical Analysis of Feature Pooling in Visual Recognition. 111-118 - Bruno Bouzy, Marc Métivier:
Multi-agent Learning Experiments on Repeated Matrix Games. 119-126 - Joseph K. Bradley, Carlos Guestrin:
Learning Tree Conditional Random Fields. 127-134 - Nader H. Bshouty, Philip M. Long:
Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering. 135-142 - Róbert Busa-Fekete, Balázs Kégl:
Fast boosting using adversarial bandits. 143-150 - Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths:
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. 151-158 - Bin Cao, Nathan Nan Liu, Qiang Yang:
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains. 159-166 - Miguel Á. Carreira-Perpiñán:
The Elastic Embedding Algorithm for Dimensionality Reduction. 167-174 - Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella:
Random Spanning Trees and the Prediction of Weighted Graphs. 175-182 - Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
Efficient Learning with Partially Observed Attributes. 183-190 - Doran Chakraborty, Peter Stone:
Convergence, Targeted Optimality, and Safety in Multiagent Learning. 191-198 - Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, Dan Roth:
Structured Output Learning with Indirect Supervision. 199-206 - Yutian Chen, Max Welling:
Dynamical Products of Experts for Modeling Financial Time Series. 207-214 - Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Label Ranking Methods based on the Plackett-Luce Model. 215-222 - Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Graded Multilabel Classification: The Ordinal Case. 223-230 - Michael H. Coen, M. Hidayath Ansari, Nathanael Fillmore:
Comparing Clusterings in Space. 231-238 - Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Two-Stage Learning Kernel Algorithms. 239-246 - Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Generalization Bounds for Learning Kernels. 247-254 - Fabrizio Costa, Kurt De Grave:
Fast Neighborhood Subgraph Pairwise Distance Kernel. 255-262 - Sajib Dasgupta, Vincent Ng:
Mining Clustering Dimensions. 263-270 - Jesse Davis, Pedro M. Domingos:
Bottom-Up Learning of Markov Network Structure. 271-278 - Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier:
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains. 279-286 - Thomas Deselaers, Vittorio Ferrari:
A Conditional Random Field for Multiple-Instance Learning. 287-294 - Joshua V. Dillon, Krishnakumar Balasubramanian, Guy Lebanon:
Asymptotic Analysis of Generative Semi-Supervised Learning. 295-302 - Frank Dondelinger, Sophie Lèbre, Dirk Husmeier:
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. 303-310 - Carlton Downey, Scott Sanner:
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda. 311-318 - Gregory Druck, Andrew McCallum:
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models. 319-326 - John C. Duchi, Lester W. Mackey, Michael I. Jordan:
On the Consistency of Ranking Algorithms. 327-334 - Krishnamurthy Dvijotham, Emanuel Todorov:
Inverse Optimal Control with Linearly-Solvable MDPs. 335-342 - Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman:
Continuous-Time Belief Propagation. 343-350 - Lev Faivishevsky, Jacob Goldberger:
Nonparametric Information Theoretic Clustering Algorithm. 351-358 - Romaric Gaudel, Michèle Sebag:
Feature Selection as a One-Player Game. 359-366 - Matan Gavish, Boaz Nadler, Ronald R. Coifman:
Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning. 367-374 - Sean Gerrish, David M. Blei:
A Language-based Approach to Measuring Scholarly Impact. 375-382 - Noam Goldberg, Jonathan Eckstein:
Boosting Classifiers with Tightened L0-Relaxation Penalties. 383-390 - Ryan Gomes, Andreas Krause:
Budgeted Nonparametric Learning from Data Streams. 391-398 - Karol Gregor, Yann LeCun:
Learning Fast Approximations of Sparse Coding. 399-406 - Alexander Grubb, J. Andrew Bagnell:
Boosted Backpropagation Learning for Training Deep Modular Networks. 407-414 - Andrew Guillory, Jeff A. Bilmes:
Interactive Submodular Set Cover. 415-422 - Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik Varma:
Large Scale Max-Margin Multi-Label Classification with Priors. 423-430 - Abhay Harpale, Yiming Yang:
Active Learning for Multi-Task Adaptive Filtering. 431-438 - Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Bayesian Nonparametric Matrix Factorization for Recorded Music. 439-446 - Jean Honorio, Dimitris Samaras:
Multi-Task Learning of Gaussian Graphical Models. 447-454 - Jonathan Huang, Carlos Guestrin:
Learning Hierarchical Riffle Independent Groupings from Rankings. 455-462 - Martial Hue, Jean-Philippe Vert:
On learning with kernels for unordered pairs. 463-470 - Martin Jaggi, Marek Sulovský:
A Simple Algorithm for Nuclear Norm Regularized Problems. 471-478 - Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf:
Telling cause from effect based on high-dimensional observations. 479-486 - Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis R. Bach:
Proximal Methods for Sparse Hierarchical Dictionary Learning. 487-494 - Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu:
3D Convolutional Neural Networks for Human Action Recognition. 495-502 - Vladimir Jojic, Stephen Gould, Daphne Koller:
Accelerated dual decomposition for MAP inference. 503-510 - Shivaram Kalyanakrishnan, Peter Stone:
Efficient Selection of Multiple Bandit Arms: Theory and Practice. 511-518 - Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon:
A scalable trust-region algorithm with application to mixed-norm regression. 519-526 - Minyoung Kim, Fernando De la Torre:
Local Minima Embedding. 527-534 - Minyoung Kim, Fernando De la Torre:
Gaussian Processes Multiple Instance Learning. 535-542 - Seyoung Kim, Eric P. Xing:
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. 543-550 - Stanley Kok, Pedro M. Domingos:
Learning Markov Logic Networks Using Structural Motifs. 551-558 - Mladen Kolar, Ankur P. Parikh, Eric P. Xing:
On Sparse Nonparametric Conditional Covariance Selection. 559-566 - Andreas Krause, Volkan Cevher:
Submodular Dictionary Selection for Sparse Representation. 567-574 - Brian Kulis, Peter L. Bartlett:
Implicit Online Learning. 575-582 - Tobias Lang, Marc Toussaint:
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds. 583-590 - Nathan Lay, Adrian Barbu:
Supervised Aggregation of Classifiers using Artificial Prediction Markets. 591-598 - Alessandro Lazaric, Mohammad Ghavamzadeh:
Bayesian Multi-Task Reinforcement Learning. 599-606 - Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
Analysis of a Classification-based Policy Iteration Algorithm. 607-614 - Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
Finite-Sample Analysis of LSTD. 615-622 - Nicolas Le Roux, Andrew W. Fitzgibbon:
A fast natural Newton method. 623-630 - Mu Li, James T. Kwok, Bao-Liang Lu:
Making Large-Scale Nyström Approximation Possible. 631-638 - Percy Liang, Michael I. Jordan, Dan Klein:
Learning Programs: A Hierarchical Bayesian Approach. 639-646 - Percy Liang, Nati Srebro:
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning. 647-654 - Frank Lin, William W. Cohen:
Power Iteration Clustering. 655-662 - Guangcan Liu, Zhouchen Lin, Yong Yu:
Robust Subspace Segmentation by Low-Rank Representation. 663-670 - Hairong Liu, Shuicheng Yan:
Robust Graph Mode Seeking by Graph Shift. 671-678 - Wei Liu, Junfeng He, Shih-Fu Chang:
Large Graph Construction for Scalable Semi-Supervised Learning. 679-686 - Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu:
Learning Temporal Causal Graphs for Relational Time-Series Analysis. 687-694 - Daniel J. Lizotte, Michael H. Bowling, Susan A. Murphy:
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis. 695-702 - Philip M. Long, Rocco A. Servedio:
Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate. 703-710 - Lester W. Mackey, David J. Weiss, Michael I. Jordan:
Mixed Membership Matrix Factorization. 711-718 - Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Richard S. Sutton:
Toward Off-Policy Learning Control with Function Approximation. 719-726 - M. M. Hassan Mahmud:
Constructing States for Reinforcement Learning. 727-734 - James Martens:
Deep learning via Hessian-free optimization. 735-742 - James Martens:
Learning the Linear Dynamical System with ASOS. 743-750 - Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy:
From Transformation-Based Dimensionality Reduction to Feature Selection. 751-758 - Hamed Masnadi-Shirazi, Nuno Vasconcelos:
Risk minimization, probability elicitation, and cost-sensitive SVMs. 759-766 - Julian J. McAuley, Tibério S. Caetano:
Exploiting Data-Independence for Fast Belief-Propagation. 767-774 - Brian McFee, Gert R. G. Lanckriet:
Metric Learning to Rank. 775-782 - Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson:
Learning Efficiently with Approximate Inference via Dual Losses. 783-790 - Martin Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang:
Deep Supervised t-Distributed Embedding. 791-798 - Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Nonparametric Return Distribution Approximation for Reinforcement Learning. 799-806 - Vinod Nair, Geoffrey E. Hinton:
Rectified Linear Units Improve Restricted Boltzmann Machines. 807-814 - Shinichi Nakajima, Masashi Sugiyama:
Implicit Regularization in Variational Bayesian Matrix Factorization. 815-822 - Sahand N. Negahban, Martin J. Wainwright:
Estimation of (near) low-rank matrices with noise and high-dimensional scaling. 823-830 - Donglin Niu, Jennifer G. Dy, Michael I. Jordan:
Multiple Non-Redundant Spectral Clustering Views. 831-838 - Santiago Ontañón, Enric Plaza:
Multiagent Inductive Learning: an Argumentation-based Approach. 839-846 - John W. Paisley, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Lawrence Carin:
A Stick-Breaking Construction of the Beta Process. 847-854 - Constantinos Panagiotakopoulos, Petroula Tsampouka:
The Margin Perceptron with Unlearning. 855-862 - David Pardoe, Peter Stone:
Boosting for Regression Transfer. 863-870 - Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. 871-878 - Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russell Greiner:
Budgeted Distribution Learning of Belief Net Parameters. 879-886 - Leonard K. M. Poon, Nevin Lianwen Zhang, Tao Chen, Yi Wang:
Variable Selection in Model-Based Clustering: To Do or To Facilitate. 887-894 - Monica Dinculescu, Doina Precup:
Approximate Predictive Representations of Partially Observable Systems. 895-902 - Joseph Reisinger, Austin Waters, Bryan Silverthorn, Raymond J. Mooney:
Spherical Topic Models. 903-910 - Stefan Rüping:
SVM Classifier Estimation from Group Probabilities. 911-918 - Daniil Ryabko:
Clustering processes. 919-926 - Yunus Saatci, Ryan D. Turner, Carl Edward Rasmussen:
Gaussian Process Change Point Models. 927-934 - Jun Sakuma, Hiromi Arai:
Online Prediction with Privacy. 935-942 - Ruslan Salakhutdinov:
Learning Deep Boltzmann Machines using Adaptive MCMC. 943-950 - Christoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer:
Active Risk Estimation. 951-958 - Bruno Scherrer:
Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view. 959-966 - Matthias W. Seeger:
Gaussian Covariance and Scalable Variational Inference. 967-974 - Ali H. Shoeb, John V. Guttag:
Application of Machine Learning To Epileptic Seizure Detection. 975-982 - Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi:
Learning optimally diverse rankings over large document collections. 983-990 - Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, Alexander J. Smola:
Hilbert Space Embeddings of Hidden Markov Models. 991-998 - Sören Sonnenburg, Vojtech Franc:
COFFIN: A Computational Framework for Linear SVMs. 999-1006 - Jonathan Sorg, Satinder Singh, Richard L. Lewis:
Internal Rewards Mitigate Agent Boundedness. 1007-1014 - Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger:
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. 1015-1022 - Zeeshan Syed, Ilan Rubinfeld:
Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes. 1023-1030 - Istvan Szita, Csaba Szepesvári:
Model-based reinforcement learning with nearly tight exploration complexity bounds. 1031-1038 - Arthur Szlam, Xavier Bresson:
Total Variation, Cheeger Cuts. 1039-1046 - Mingkui Tan, Li Wang, Ivor W. Tsang:
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. 1047-1054 - Yichuan Tang, Chris Eliasmith:
Deep networks for robust visual recognition. 1055-1062 - Mamadou Thiao, Pham Dinh Tao, Le Thi Hoai An:
A DC Programming Approach for Sparse Eigenvalue Problem. 1063-1070 - Christophe Thiery, Bruno Scherrer:
Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems. 1071-1078 - Daniel Ting, Ling Huang, Michael I. Jordan:
An Analysis of the Convergence of Graph Laplacians. 1079-1086 - Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima:
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. 1087-1094 - Han-Hsing Tu, Hsuan-Tien Lin:
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification. 1095-1102 - David Vickrey, Cliff Chiung-Yu Lin, Daphne Koller:
Non-Local Contrastive Objectives. 1103-1110 - Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth:
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. 1111-1118 - Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos Diuk:
Generalizing Apprenticeship Learning across Hypothesis Classes. 1119-1126 - Jun Wang, Sanjiv Kumar, Shih-Fu Chang:
Sequential Projection Learning for Hashing with Compact Codes. 1127-1134 - Wei Wang, Zhi-Hua Zhou:
A New Analysis of Co-Training. 1135-1142 - Zhuang Wang, Koby Crammer, Slobodan Vucetic:
Multi-Class Pegasos on a Budget. 1143-1150 - Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei:
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. 1151-1158 - Xindong Wu, Kui Yu, Hao Wang, Wei Ding:
Online Streaming Feature Selection. 1159-1166 - Michael Wunder, Michael L. Littman, Monica Babes:
Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration. 1167-1174 - Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael R. Lyu:
Simple and Efficient Multiple Kernel Learning by Group Lasso. 1175-1182 - Feng Yan, Yuan (Alan) Qi:
Sparse Gaussian Process Regression via L1 Penalization. 1183-1190 - Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Lyu:
Online Learning for Group Lasso. 1191-1198 - Tianbao Yang, Rong Jin, Anil K. Jain:
Learning from Noisy Side Information by Generalized Maximum Entropy Model. 1199-1206 - Huizhen Yu:
Convergence of Least Squares Temporal Difference Methods Under General Conditions. 1207-1214 - Kai Yu, Tong Zhang:
Improved Local Coordinate Coding using Local Tangents. 1215-1222 - Yi Zhang, Jeff G. Schneider:
Projection Penalties: Dimension Reduction without Loss. 1223-1230 - Peilin Zhao, Steven C. H. Hoi:
OTL: A Framework of Online Transfer Learning. 1231-1238 - Jun Zhu, Eric P. Xing:
Conditional Topic Random Fields. 1239-1246 - Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish:
Cognitive Models of Test-Item Effects in Human Category Learning. 1247-1254 - Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
Modeling Interaction via the Principle of Maximum Causal Entropy. 1255-1262
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