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ORCIDJournal of Machine Learning Research, Volume 13
Volume 13, 2012
- Yiming Ying, Peng Li:
Distance Metric Learning with Eigenvalue Optimization. 1-26 - Gavin Brown, Adam Craig Pocock, Ming-Jie Zhao, Mikel Luján:
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection. 27-66 - Stanislav Minsker:
Plug-in Approach to Active Learning. 67-90 - Haizhang Zhang, Yuesheng Xu, Qinghui Zhang:
Refinement of Operator-valued Reproducing Kernels. 91-136 - Nir Ailon:
An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity. 137-164 - Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao:
Optimal Distributed Online Prediction Using Mini-Batches. 165-202 - Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia:
Active Clustering of Biological Sequences. 203-225 - Francesco Orabona, Jie Luo, Barbara Caputo:
Multi Kernel Learning with Online-Batch Optimization. 227-253 - Ran El-Yaniv, Yair Wiener:
Active Learning via Perfect Selective Classification. 255-279 - James Bergstra, Yoshua Bengio:
Random Search for Hyper-Parameter Optimization. 281-305 - Michael Gutmann, Aapo Hyvärinen:
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics. 307-361 - Gary B. Huang, Andrew Kae, Carl Doersch, Erik G. Learned-Miller:
Bounding the Probability of Error for High Precision Optical Character Recognition. 363-387 - Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming. 389-427 - Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in the Embedded Manifold of Low-rank Matrices. 429-458 - Mario Frank, Andreas P. Streich, David A. Basin, Joachim M. Buhmann:
Multi-Assignment Clustering for Boolean Data. 459-489 - Vikas C. Raykar, Shipeng Yu:
Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks. 491-518 - Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning Using a Linear Transformation. 519-547 - Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl:
MULTIBOOST: A Multi-purpose Boosting Package. 549-553 - Stephen R. Piccolo, Lewis J. Frey:
ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel. 555-559 - Matus Telgarsky:
A Primal-Dual Convergence Analysis of Boosting. 561-606 - Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muhammad Atif Tahir:
Non-Sparse Multiple Kernel Fisher Discriminant Analysis. 607-642 - Hugo Larochelle, Michael I. Mandel, Razvan Pascanu, Yoshua Bengio:
Learning Algorithms for the Classification Restricted Boltzmann Machine. 643-669 - Andreas Maurer, Massimiliano Pontil:
Structured Sparsity and Generalization. 671-690 - Grigorios Skolidis, Guido Sanguinetti:
A Case Study on Meta-Generalising: A Gaussian Processes Approach. 691-721 - Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test. 723-773 - Chiwoo Park, Jianhua Z. Huang, Yu Ding:
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression. 775-779 - Rahul Mazumder, Trevor Hastie:
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. 781-794 - Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Algorithms for Learning Kernels Based on Centered Alignment. 795-828 - Roland R. Ramsahai:
Causal Bounds and Observable Constraints for Non-deterministic Models. 829-848 - Marinka Zitnik, Blaz Zupan:
NIMFA: A Python Library for Nonnegative Matrix Factorization. 849-853 - Franz J. Király, Paul von Bünau, Frank C. Meinecke, Duncan A. J. Blythe, Klaus-Robert Müller:
Algebraic Geometric Comparison of Probability Distributions. 855-903 - Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Larry A. Wasserman:
Stability of density-based clustering. 905-948 - Gil Tahan, Lior Rokach, Yuval Shahar:
Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features. 949-979 - Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Sampling Methods for the Nyström Method. 981-1006 - Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel:
Positive Semidefinite Metric Learning Using Boosting-like Algorithms. 1007-1036 - Yongdai Kim, Sunghoon Kwon, Hosik Choi:
Consistent Model Selection Criteria on High Dimensions. 1037-1057 - Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman:
The huge Package for High-dimensional Undirected Graph Estimation in R. 1059-1062 - Gérard Biau:
Analysis of a Random Forests Model. 1063-1095 - Ioannis Tsamardinos, Sofia Triantafilou, Vincenzo Lagani:
Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies. 1097-1157 - David Chiang:
Hope and Fear for Discriminative Training of Statistical Translation Models. 1159-1187 - Ji Liu, Peter Wonka, Jieping Ye:
A Multi-Stage Framework for Dantzig Selector and LASSO. 1189-1219 - Benjamin I. P. Rubinstein, J. Hyam Rubinstein:
A Geometric Approach to Sample Compression. 1221-1261 - Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman:
Minimax Manifold Estimation. 1263-1291 - Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar:
Query Strategies for Evading Convex-Inducing Classifiers. 1293-1332 - George Dimitri Konidaris, Ilya Scheidwasser, Andrew G. Barto:
Transfer in Reinforcement Learning via Shared Features. 1333-1371 - Wojciech Rejchel:
On Ranking and Generalization Bounds. 1373-1392 - Le Song, Alexander J. Smola, Arthur Gretton, Justin Bedo, Karsten M. Borgwardt:
Feature Selection via Dependence Maximization. 1393-1434 - Zhiwei (Tony) Qin, Donald Goldfarb:
Structured Sparsity via Alternating Direction Methods. 1435-1468 - Steve Hanneke:
Activized Learning: Transforming Passive to Active with Improved Label Complexity. 1469-1587 - Aleix M. Martínez, Shichuan Du:
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives. 1589-1608 - Neil D. Lawrence:
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models. 1609-1638 - Tim van Erven, Mark D. Reid, Robert C. Williamson:
Mixability is Bayes Risk Curvature Relative to Log Loss. 1639-1663 - Sahand N. Negahban, Martin J. Wainwright:
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise. 1665-1697 - Hannes Nickisch:
glm-ie: Generalised Linear Models Inference & Estimation Toolbox. 1699-1703 - Sangkyun Lee, Stephen J. Wright:
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning. 1705-1744 - Kian Ming Adam Chai:
Variational Multinomial Logit Gaussian Process. 1745-1808 - Philipp Hennig, Christian J. Schuler:
Entropy Search for Information-Efficient Global Optimization. 1809-1837 - Jian Huang, Cun-Hui Zhang:
Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications. 1839-1864 - Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari:
Regularization Techniques for Learning with Matrices. 1865-1890 - Koby Crammer, Mark Dredze, Fernando Pereira:
Confidence-Weighted Linear Classification for Text Categorization. 1891-1926 - Aviv Tamar, Dotan Di Castro, Ron Meir:
Integrating a Partial Model into Model Free Reinforcement Learning. 1927-1966 - Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse:
Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences. 1967-1971 - Lan Xue, Annie Qu:
Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality. 1973-1998 - Guo-Xun Yuan, Chia-Hua Ho, Chih-Jen Lin:
An Improved GLMNET for L1-regularized Logistic Regression. 1999-2030 - Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan:
EP-GIG Priors and Applications in Bayesian Sparse Learning. 2031-2061 - Tom De Smedt, Walter Daelemans:
Pattern for Python. 2063-2067 - Benedict C. May, Nathan Korda, Anthony Lee, David S. Leslie:
Optimistic Bayesian Sampling in Contextual-Bandit Problems. 2069-2106 - Christopher R. Genovese, Jiashun Jin, Larry A. Wasserman, Zhigang Yao:
A Comparison of the Lasso and Marginal Regression. 2107-2143 - David P. Helmbold, Philip M. Long:
On the necessity of irrelevant variables. 2145-2170 - Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné:
DEAP: evolutionary algorithms made easy. 2171-2175 - Adrian Barbu, Nathan Lay:
An introduction to artificial prediction markets for classification. 2177-2204 - Helen Cooper, Eng-Jon Ong, Nicolas Pugeault, Richard Bowden:
Sign language recognition using sub-units. 2205-2231 - Jia Zeng:
A topic modeling toolbox using belief propagation. 2233-2236 - Jun Zhu, Amr Ahmed, Eric P. Xing:
MedLDA: maximum margin supervised topic models. 2237-2278 - Carl Brunner, Andreas Fischer, Klaus Luig, Thorsten Thies:
Pairwise support vector machines and their application to large scale problems. 2279-2292 - Animashree Anandkumar, Vincent Y. F. Tan, Furong Huang, Alan S. Willsky:
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion. 2293-2337 - Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi:
A local spectral method for graphs: with applications to improving graph partitions and exploring data graphs locally. 2339-2365 - Timo Aho, Bernard Zenko, Saso Dzeroski, Tapio Elomaa:
Multi-target regression with rule ensembles. 2367-2407 - Alain Hauser, Peter Bühlmann:
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. 2409-2464 - Marius Kloft, Gilles Blanchard:
On the convergence rate of lp-norm multiple kernel learning. 2465-2502 - Mehrdad Mahdavi, Rong Jin, Tianbao Yang:
Trading regret for efficiency: online convex optimization with long term constraints. 2503-2528 - JooSeuk Kim, Clayton D. Scott:
Robust kernel density estimation. 2529-2565 - Jasper Snoek, Ryan P. Adams, Hugo Larochelle:
Nonparametric guidance of autoencoder representations using label information. 2567-2588 - Sunita Nayak, Kester Duncan, Sudeep Sarkar, Barbara L. Loeding:
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs. 2589-2615 - Michael Brückner, Christian Kanzow, Tobias Scheffer:
Static prediction games for adversarial learning problems. 2617-2654 - Ofer Dekel, Claudio Gentile, Karthik Sridharan:
Selective sampling and active learning from single and multiple teachers. 2655-2697 - Joonseok Lee, Mingxuan Sun, Guy Lebanon:
PREA: personalized recommendation algorithms toolkit. 2699-2703 - Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan:
Coherence functions with applications in large-margin classification methods. 2705-2734 - Odalric-Ambrym Maillard, Rémi Munos:
Linear regression with random projections. 2735-2772 - Matthieu Solnon, Sylvain Arlot, Francis R. Bach:
Multi-task regression using minimal penalties. 2773-2812 - José Hernández-Orallo, Peter A. Flach, César Ferri:
A unified view of performance metrics: translating threshold choice into expected classification loss. 2813-2869 - Dominik Schnitzer, Arthur Flexer, Markus Schedl, Gerhard Widmer:
Local and global scaling reduce hubs in space. 2871-2902 - Elad Hazan, Satyen Kale:
Online submodular minimization. 2903-2922 - Aharon Ben-Tal, Sahely Bhadra, Chiranjib Bhattacharyya, Arkadi Nemirovski:
Efficient methods for robust classification under uncertainty in kernel matrices. 2923-2954 - Xiaogang Su, Joseph Kang, Juanjuan Fan, Richard A. Levine, Xin Yan:
Facilitating score and causal inference trees for large observational studies. 2955-2994 - David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski:
Oger: modular learning architectures for large-scale sequential processing. 2995-2998 - Sivan Sabato, Naftali Tishby:
Multi-instance learning with any hypothesis class. 2999-3039 - Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
Finite-sample analysis of least-squares policy iteration. 3041-3074 - Yang Wang, Duan Tran, Zicheng Liao, David A. Forsyth:
Discriminative hierarchical part-based models for human parsing and action recognition. 3075-3102 - Zhuang Wang, Koby Crammer, Slobodan Vucetic:
Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training. 3103-3131 - Kay Henning Brodersen, Christoph Mathys, Justin R. Chumbley, Jean Daunizeau, Cheng Soon Ong, Joachim M. Buhmann, Klaas E. Stephan:
Bayesian mixed-effects inference on classification performance in hierarchical data sets. 3133-3176 - Tamer Salman, Yoram Baram:
Quantum set intersection and its application to associative memory. 3177-3206 - Mohammad Gheshlaghi Azar, Vicenç Gómez, Hilbert J. Kappen:
Dynamic policy programming. 3207-3245 - Konrad Rieck, Christian Wressnegger, Alexander Bikadorov:
Sally: a tool for embedding strings in vector spaces. 3247-3251 - Daniel J. Lizotte, Michael Bowling, Susan A. Murphy:
Linear fitted-Q iteration with multiple reward functions. 3253-3295 - Yui Man Lui:
Human gesture recognition on product manifolds. 3297-3321 - Chia-Hua Ho, Chih-Jen Lin:
Large-scale linear support vector regression. 3323-3348 - Nicolas Gillis:
Sparse and unique nonnegative matrix factorization through data preprocessing. 3349-3386 - Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Learning linear cyclic causal models with latent variables. 3387-3439 - Karthik Mohan, Maryam Fazel:
Iterative reweighted algorithms for matrix rank minimization. 3441-3473 - Petros Drineas
, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. 3475-3506 - Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun:
PAC-bayes bounds with data dependent priors. 3507-3531 - Stephen Gould:
DARWIN: a framework for machine learning and computer vision research and development. 3533-3537 - Trinh Minh Tri Do, Thierry Artières:
Regularized bundle methods for convex and non-convex risks. 3539-3583 - Daniel Le Ly, Hod Lipson:
Learning symbolic representations of hybrid dynamical systems. 3585-3618 - Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu:
SVDFeature: a toolkit for feature-based collaborative filtering. 3619-3622 - Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Smoothing multivariate performance measures. 3623-3680 - Marius Kloft, Pavel Laskov:
Security analysis of online centroid anomaly detection. 3681-3724 - Tobias Lang, Marc Toussaint, Kristian Kersting:
Exploration in relational domains for model-based reinforcement learning. 3725-3768
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