WELCOME to ICML 2011! The Twenty-Eighth International Conference on Machine Learning (ICML 2011) is held at the Hyatt Regency Bellevue between June 28 and July 2, 2011. Welcome to Bellevue! All technical talks will take place in the Grand Ballroom on the second Floor oF the hotel’s conFerence center. The conference registration desk is located on the second Floor next to the Grand Ballroom. Each talk oF the main conFerence is allocated 20 minutes plus 5 minutes For questions and speaker transition. Each talk will also have an accompanying poster in one oF the poster sessions to allow one-on-one discussions. The Poster Sessions will take place in the Evergreen Ballroom (First Floor, lobby level) on Wednesday, June 29 and Friday, July 1, in the evening starting at 6pm. A dinner buFFet will be served during both poster sessions. A Message BoarD is located near the registration desk. An electronic version oF the message board will be available on the conFerence homepage. Internet access. Wireless Internet is available in the conFerence area Free oF charge For conFerence registrants. Please use the network Hyatt-MEETING and passcode ICML2011. For the guestrooms of conference attendees, the Hyatt oFFers Free wired internet access, but charges a daily Fee For wireless internet. The full conference proceedings are available via conference web site at icml-2011.org. The joint ICML/ACL/ISCA Symposium on Machine Learning in Speech and Language Processing is held on Monday, June 27, 2011, in Grand-IJ. All ICML main conFerence registrants are welcome to attend. The six ICML Tutorials are held on Tuesday, June 28, 2011 in two parallel sessions. The nine ICML Workshops are held on Saturday, July 2, 2011. Throughout the conFerence, the coffee breaks are held in the Foyer oF the Grand Ballroom, just outside the conFerence rooms. The ICML Banquet sponsored by Amazon.com, is on Thursday, June 30, at the Museum oF Flight in Seattle, a 30-minute drive From Bellevue. Transportation will be provided from the Bellevue Hyatt starting at 4:50 p.m. The buses will be leaving From the Evergreen North Foyer, on the First Floor, just outside the Evergreen Ballroom. The IMLS Business Meeting takes place on Friday, July 1, From 5:30 to 6:30 p.m. in Oren Hall. The business meeting is open to all attendees. The Machine Learning journal (MLJ) boarD meeting takes place on Wednesday, June 29 at noon. The Women in Machine Learning (WiML) event takes place on Thursday, June 30 at noon. The IMLS BoarD Meeting takes place on Friday, July 1 at noon. Exhibitors: Amazon.com, Springer, Cambridge University Press, and Morgan-Claypool have desks that will be located in the Grand Ballroom Foyer throughout. 1 Program Schedule Monday, June 27, 2011 - Joint ICML/ACL/ISCA Event Symposium on Machine Learning in Speech and Language Processing Venue: Grand-IJ (Grand Ballroom, room IJ) Registration opens at 7am. The symposium starts at 8am. Tuesday, June 28, 2011 – ICML Tutorials Time Room Tutorial Organizer(s) Machine Learning for Large Scale Grand-AB Deepak Agarwal, Bee-Chung Chen 9:00am- Recommender Systems 12:00pm Grand-EF Machine Learning and Robotics Marc Toussaint Introduction to Bandits: Algorithms and Grand-AB Jean-Yves Audibert, Remi Munos 1:00pm- Theory 4:00pm Grand-EF Collective Intelligence and Machine Learning Haym Hirsch Grand-AB Learning Kernels Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh 4:30pm- Machine Learning in Ecological Science and 7:30pm Grand-EF Tom Dietterich, Rebecca Hutchinson, Daniel Sheldon Environmental Policy Main Conference (Wednesday, Jun 29 - Fri. July 2) Room Numbers and Configuration. 2 Wednesday, June 29, 2011 Time Room Session Presentation/Event Authors/Presenters Lise Getoor, Tobias Scheffer, 8:30–9:00 A Welcome Welcome address and Best Paper Awards Dragos Margineantu Keynote Embracing Uncertainty: Applied Machine Learning 9:00–10:00 A Christopher Bishop Chair: John Platt Comes of Age 10:00–10:30 Coffee Break Unimodal Bandits Jia Yuan Yu; Shie Mannor 2A On tracking portfolios with certainty equivalents on a Richard Nock; Brice Magdalou; Bandits and generalization of Markowitz model: the Fool, the Wise Online Learning Eric Briys; Frank Nielsen A and the Adaptive Area chair: Beat the Mean Bandit Yisong Yue; Thorsten Joachims John Langford Multiclass Classification with Bandit Feedback using Koby Crammer; C. Gentile Adaptive Regularization An Augmented Lagrangian Approach to Constrained Andre Martins et al. 2I MAP Inference Structured Output Max-margin Learning for Lower Linear Envelope I Stephen Gould Potentials in Binary Markov Random Fields Area chair: Inference of Inversion Transduction Grammars Alexander Clark Mehryar Mohri Minimal Loss Hashing for Compact Binary Codes Mohammad Norouzi; David Fleet Structure Learning in Ergodic Factored MDPs without 2E Doran Chakraborty; Peter Stone Reinforcement Knowledge of the Transition Function's In-Degree Learning E The Infinite Regionalized Policy Representation Miao Liu; X. Liao; L. Carin 10:30–12:10 Area chair: Online Discovery of Feature Dependencies Alborz Geramifard et al. Ron Parr Doubly Robust Policy Evaluation and Learning M. Dudik; J. Langford; L. Li Dynamic Tree Block Coordinate Ascent Daniel Tarlow et al. 2F Approximation Bounds for Inference using Graphical Stefanie Jegelka; Jeff Bilmes Models and Cooperative Cuts F Optimi-zation Convex Max-Product over Compact Sets for Protein Jian Peng; Tamir Hazan; David Folding McAllester; Raquel Urtasun Area chair: On the Use of Variational Inference for Learning Nando de Freitas Eunho Yang; Pradeep Ravikumar Discrete Graphical Models GoDec: Randomized Low-rank and Sparse Matrix Tianyi Zhou; Dacheng Tao 2G Decomposition in Noisy Case Recommendation and Matrix Large-Scale Convex Minimization with a Low-Rank Shai Shalev-Shwartz; Alon G Factorization Constraint Gonen; Ohad Shamir Linear Regression under Fixed-Rank Constraints: A Gilles Meyer; Silvère Bonnabel; Area chair: Riemannian Approach Rodolphe Sepulchre Dale Schuurmans Clustering by Left-Stochastic Matrix Factorization Raman Arora et al. Lunch Break 12:10–1:40 Spruce MLJ Editorial Board Meeting Jascha Sohl-Dickstein; Peter Minimum Probability Flow Learning 3A Battaglino; Michael DeWeese Neural Networks The Importance of Encoding Versus Training with and Statistical Adam Coates; Andrew Ng A Methods Sparse Coding and Vector Quantization Learning Recurrent Neural Networks with Hessian- James Martens; Ilya Sutskever Area chair: Free Optimization Thore Graepel On Random Weights Unsupervised Feature Learning Andrew Saxe et al. On the Integration of Topic Modeling and Dictionary Lingbo Li; Mingyuan Zhou; 3I Learning Guillermo Sapiro; Lawrence Carin Latent-Variable Beam Search based MAP Estimates for the Indian Models Piyush Rai; Hal Daume III I Buffet Process 1:40–3:20 Area chair: Tree-Structured Infinite Sparse Factor Model X. Zhang; D. Dunson; L. Carin Alexander Ihler Jacob Eisenstein; Amr Ahmed; Sparse Additive Generative Models of Text Eric Xing Wei Liu; Jun Wang; Sanjiv Hashing with Graphs Kumar; Shih-Fu Chang 3E Large-Scale Large Scale Text Classification using Semi-supervised Jiang Su; Jelber Sayyad Shirab; Learning Multinomial Naive Bayes Stan Matwin E Parallel Coordinate Descent for L1-Regularized Loss Joseph Bradley; Aapo Kyrola; Area chair: Minimization Daniel Bickson; Carlos Guestrin Rich Caruana OptiML: An Implicitly Parallel Domain-Specific Arvind Sujeeth et al. Language for Machine Learning 3 On the Necessity of Irrelevant Variables Dave Helmbold; Phil Long Pascal Germain; Alexandre A PAC-Bayes Sample-compression Approach to Kernel 3F Lacoste; Francois Laviolette; Learning Theory Methods Mario Marchand; Sara Shanian F Simultaneous Learning and Covering with Adversarial Area chair: Andrew Guillory; Jeff Bilmes Sally Goldman Noise Darío García-García; Ulrike von Risk-Based Generalizations of f-divergences Luxburg; Raúl Santos-Rodríguez Yi Jiang; Jiangtao Ren 3G Eigenvalue Sensitive Feature Selection Feature Dijun Luo; Chris Ding; Feiping Cauchy Graph Embedding Selection, Nie; Heng Huang Dimensionality Albert Shieh; Tatsunori G Tree preserving embedding Reduction Hashimoto; Edo Airoldi Area chair: Pierre Machart; Thomas Peel; Stochastic Low-Rank Kernel Learning for Regression Sandrine Anthoine; Liva Corinna Cortes Ralaivola; Hervé Glotin, 3:20–3:50 Coffee Break Debt Collections Using Constrained Reinforcement 4A Naoki Abe et al. Invited Cross- Learning Conference Modeling Mutual Context of Object and Human Pose Bangpeng Yao; Aditya Khosla; Li Track A in Human-Object Interaction Activities Fei-Fei Area chair: Efficient Planning under Uncertainty for a Target- Abraham Bachrach; Ruijie He; Dragos Tracking Micro-Aerial Vehicle in Urban Environments Nicholas Roy Margineantu Gesture-Based Human-Robot Jazz Improvisation Gil Weinberg Loris Bazzani; Nando Freitas; Learning attentional policies for tracking and Hugo Larochelle; Vittorio recognition in video with deep networks 4I Murino; Jo-Anne Ting Neural Networks Jiquan Ngiam; Zhenghao Chen; Learning Deep Energy Models and Deep Pang Wei Koh; Andrew Ng I Learning Unsupervised Models of Images by Spike-and-Slab Aarron Courville; James Bergstra; Area chair: RBMs Yoshua Bengio Eric Xing Kevin Swersky; Marc'Aurelio On Autoencoders and Score Matching for Energy Ranzato; David Buchman; Based Models Benjamin Marlin; Nando Freitas Haojun Chen; David Dunson; Topic Modeling with Nonparametric Markov Tree Lawrence Carin 4E Infinite SVM: a Dirichlet Process Mixture of Large- Latent-Variable Jun Zhu; Ning Chen; Eric Xing 3:50–5:30 Models margin Kernel