ICML 2012
243 papers
A Binary Classification Framework for Two-Stage Multiple Kernel Learning
Abhishek Kumar, Alexandru Niculescu-Mizil, Koray Kavukcuoglu, Hal Daumé Iii A Dantzig Selector Approach to Temporal Difference Learning
Matthieu Geist, Bruno Scherrer, Alessandro Lazaric, Mohammad Ghavamzadeh A Generative Process for Contractive Auto-Encoders
Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio A Joint Model of Language and Perception for Grounded Attribute Learning
Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox A Split-Merge Framework for Comparing Clusterings
Qiaoliang Xiang, Qi Mao, Kian Ming Adam Chai, Hai Leong Chieu, Ivor W. Tsang, Zhendong Zhao A Topic Model for Melodic Sequences
Athina Spiliopoulou, Amos J. Storkey A Unified Robust Classification Model
Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori Active Learning for Matching Problems
Laurent Charlin, Richard S. Zemel, Craig Boutilier Agglomerative Bregman Clustering
Matus Telgarsky, Sanjoy Dasgupta An Efficient Approach to Sparse Linear Discriminant Analysis
Luis Francisco Sánchez Merchante, Yves Grandvalet, Gérard Govaert An Infinite Latent Attribute Model for Network Data
Konstantina Palla, David A. Knowles, Zoubin Ghahramani An Iterative Locally Linear Embedding Algorithm
Deguang Kong, Chris H. Q. Ding, Heng Huang, Feiping Nie Anytime Marginal MAP Inference
Denis Deratani Mauá, Cassio Polpo de Campos Approximate Modified Policy Iteration
Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, Matthieu Geist Approximate Principal Direction Trees
Mark McCartin-Lim, Andrew McGregor, Rui Wang Batch Active Learning via Coordinated Matching
Javad Azimi, Alan Fern, Xiaoli Zhang Fern, Glencora Borradaile, Brent Heeringa Bayesian Optimal Active Search and Surveying
Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, Richard P. Mann Bayesian Watermark Attacks
Ivo Shterev, David B. Dunson Building High-Level Features Using Large Scale Unsupervised Learning
Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeffrey Dean, Andrew Y. Ng Canonical Trends: Detecting Trend Setters in Web Data
Felix Bießmann, Jens-Michalis Papaioannou, Mikio L. Braun, Andreas Harth Communications Inspired Linear Discriminant Analysis
Minhua Chen, William R. Carson, Miguel R. D. Rodrigues, Lawrence Carin, A. Robert Calderbank Compact Hyperplane Hashing with Bilinear Functions
Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang Comparison-Based Learning with Rank Nets
Amin Karbasi, Stratis Ioannidis, Laurent Massoulié Conditional Mean Embeddings as Regressors
Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil Consistent Multilabel Ranking Through Univariate Losses
Krzysztof Dembczynski, Wojciech Kotlowski, Eyke Hüllermeier Copula-Based Kernel Dependency Measures
Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider Data-Driven Web Design
Ranjitha Kumar, Jerry O. Talton, Salman Ahmad, Scott R. Klemmer Deep Lambertian Networks
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton Deep Mixtures of Factor Analysers
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
Jesse Davis, Vítor Santos Costa, Elizabeth Berg, David Page, Peggy L. Peissig, Michael Caldwell Distributed Tree Kernels
Fabio Massimo Zanzotto, Lorenzo Dell'Arciprete Efficient Active Algorithms for Hierarchical Clustering
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh Exact Soft Confidence-Weighted Learning
Steven C. H. Hoi, Jialei Wang, Peilin Zhao Fast Approximation of Matrix Coherence and Statistical Leverage
Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff Fast Classification Using Sparse Decision DAGs
Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl Feature Selection via Probabilistic Outputs
Andrea Pohoreckyj Danyluk, Nicholas Arnosti Gaussian Process Regression Networks
Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani Group Sparse Additive Models
Junming Yin, Xi Chen, Eric P. Xing High Dimensional Semiparametric Gaussian Copula Graphical Models
Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman Hybrid Batch Bayesian Optimization
Javad Azimi, Ali Jalali, Xiaoli Zhang Fern Hypothesis Testing Using Pairwise Distances and Associated Kernels
Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu Is Margin Preserved After Random Projection?
Qinfeng Shi, Chunhua Shen, Rhys Hill, Anton van den Hengel Latent Collaborative Retrieval
Jason Weston, Chong Wang, Ron J. Weiss, Adam Berenzweig Learning Efficient Structured Sparse Models
Alexander M. Bronstein, Pablo Sprechmann, Guillermo Sapiro Learning Force Control Policies for Compliant Robotic Manipulation
Mrinal Kalakrishnan, Ludovic Righetti, Peter Pastor, Stefan Schaal Learning Parameterized Skills
Bruno Castro da Silva, George Dimitri Konidaris, Andrew G. Barto Learning the Experts for Online Sequence Prediction
Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson Linear Off-Policy Actor-Critic
Thomas Degris, Martha White, Richard S. Sutton Lognormal and Gamma Mixed Negative Binomial Regression
Mingyuan Zhou, Lingbo Li, David B. Dunson, Lawrence Carin Manifold Relevance Determination
Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence Marginalized Denoising Autoencoders for Domain Adaptation
Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha Maximum Margin Output Coding
Yi Zhang, Jeff G. Schneider Modeling Images Using Transformed Indian Buffet Processes
Ke Zhai, Yuening Hu, Jordan L. Boyd-Graber, Sinead Williamson Modelling Transition Dynamics in MDPs with RKHS Embeddings
Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton Monte Carlo Bayesian Reinforcement Learning
Yi Wang, Kok Sung Won, David Hsu, Wee Sun Lee Near-Optimal BRL Using Optimistic Local Transitions
Mauricio Araya-López, Olivier Buffet, Vincent Thomas Nonparametric Link Prediction in Dynamic Networks
Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan Nonparametric Variational Inference
Samuel Gershman, Matthew D. Hoffman, David M. Blei On Causal and Anticausal Learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij On Local Regret
Michael Bowling, Martin Zinkevich Optimizing F-Measure: A Tale of Two Approaches
Nan Ye, Kian Ming Adam Chai, Wee Sun Lee, Hai Leong Chieu Output Space Search for Structured Prediction
Janardhan Rao Doppa, Alan Fern, Prasad Tadepalli PAC Subset Selection in Stochastic Multi-Armed Bandits
Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone Plug-in Martingales for Testing Exchangeability On-Line
Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Volodya Vovk Predicting Accurate Probabilities with a Ranking Loss
Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado Residual Components Analysis
Alfredo A. Kalaitzis, Neil D. Lawrence Robust Classification with Adiabatic Quantum Optimization
Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan, Hartmut Neven Sparse Additive Functional and Kernel CCA
Sivaraman Balakrishnan, Kriti Puniyani, John D. Lafferty The Big Data Bootstrap
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan The Greedy Miser: Learning Under Test-Time Budgets
Zhixiang Eddie Xu, Kilian Q. Weinberger, Olivier Chapelle The Kernelized Stochastic Batch Perceptron
Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro