ICML 2011
152 papers
A PAC-Bayes Sample-Compression Approach to Kernel Methods
Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian Active Learning from Crowds
Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy Adaptively Learning the Crowd Kernel
Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Kalai An Augmented Lagrangian Approach to Constrained MAP Inference
André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing Apprenticeship Learning About Multiple Intentions
Monica Babes, Vukosi Marivate, Kaushik Subramanian, Michael L. Littman Bayesian CCA via Group Sparsity
Seppo Virtanen, Arto Klami, Samuel Kaski Beat the Mean Bandit
Yisong Yue, Thorsten Joachims Bipartite Ranking Through Minimization of Univariate Loss
Wojciech Kotlowski, Krzysztof Dembczynski, Eyke Hüllermeier Cauchy Graph Embedding
Dijun Luo, Chris H. Q. Ding, Feiping Nie, Heng Huang Classification-Based Policy Iteration with a Critic
Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Bruno Scherrer Clustering by Left-Stochastic Matrix Factorization
Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, Yoshua Bengio Convex Max-Product over Compact Sets for Protein Folding
Jian Peng, Tamir Hazan, David A. McAllester, Raquel Urtasun Dynamic Egocentric Models for Citation Networks
Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth Dynamic Tree Block Coordinate Ascent
Daniel Tarlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov Efficient Rule Ensemble Learning Using Hierarchical Kernels
Pratik Jawanpuria, Jagarlapudi Saketha Nath, Ganesh Ramakrishnan Finite-Sample Analysis of Lasso-TD
Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman Functional Regularized Least Squares Classication with Operator-Valued Kernels
Hachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy Generating Text with Recurrent Neural Networks
Ilya Sutskever, James Martens, Geoffrey E. Hinton Hashing with Graphs
Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang Infinite Dynamic Bayesian Networks
Finale Doshi, David Wingate, Joshua B. Tenenbaum, Nicholas Roy Learning Deep Energy Models
Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, Andrew Y. Ng Learning Output Kernels with Block Coordinate Descent
Francesco Dinuzzo, Cheng Soon Ong, Peter V. Gehler, Gianluigi Pillonetto Mapping Kernels for Trees
Kilho Shin, Marco Cuturi, Tetsuji Kuboyama Minimum Probability Flow Learning
Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese Multimodal Deep Learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng Multiple Instance Learning with Manifold Bags
Boris Babenko, Nakul Verma, Piotr Dollár, Serge J. Belongie On Autoencoders and Score Matching for Energy Based Models
Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas On Optimization Methods for Deep Learning
Quoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng On Random Weights and Unsupervised Feature Learning
Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng Online AUC Maximization
Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang Online Discovery of Feature Dependencies
Alborz Geramifard, Finale Doshi, Josh Redding, Nicholas Roy, Jonathan P. How Optimal Distributed Online Prediction
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning
Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning Preserving Personalized Pagerank in Subgraphs
Andrea Vattani, Deepayan Chakrabarti, Maxim Gurevich Probabilistic Matrix Addition
Amrudin Agovic, Arindam Banerjee, Snigdhansu Chatterjee Risk-Based Generalizations of F-Divergences
Dario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez Robust Matrix Completion and Corrupted Columns
Yudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi SampleRank: Training Factor Graphs with Atomic Gradients
Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum Sparse Additive Generative Models of Text
Jacob Eisenstein, Amr Ahmed, Eric P. Xing Speeding-up Hoeffding-Based Regression Trees with Options
Elena Ikonomovska, João Gama, Bernard Zenko, Saso Dzeroski Stochastic Low-Rank Kernel Learning for Regression
Pierre Machart, Thomas Peel, Sandrine Anthoine, Liva Ralaivola, Hervé Glotin Support Vector Machines as Probabilistic Models
Vojtech Franc, Alexander Zien, Bernhard Schölkopf The Constrained Weight Space SVM: Learning with Ranked Features
Kevin Small, Byron C. Wallace, Carla E. Brodley, Thomas A. Trikalinos Topic Modeling with Nonparametric Markov Tree
Haojun Chen, David B. Dunson, Lawrence Carin Tree Preserving Embedding
Albert Shieh, Tatsunori B. Hashimoto, Edoardo M. Airoldi Tree-Structured Infinite Sparse Factor Model
XianXing Zhang, David B. Dunson, Lawrence Carin Uncovering the Temporal Dynamics of Diffusion Networks
Manuel Gomez-Rodriguez, David Balduzzi, Bernhard Schölkopf Unimodal Bandits
Jia Yuan Yu, Shie Mannor