ICML2009 Sessions

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Monday, June 15th

9:30AM - 9:50AM	Welcome	Leacock 132
9:50AM - 10:20AM	Coffee break
10:20AM - 12:20PM	1A - Clustering Geometry	Room: A
	Chair: Kiri Wagstaff
	#95: Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning (Nowozin, Jegelka)    #316: A Scalable Framework for Discovering Coherent Co-clusters in Noisy Data (Deodhar, Ghosh, Gupta, Cho, Dhillon)    #317: Multi-View Clustering via Canonical Correlation Analysis (Chaudhuri, Kakade, Livescu, Sridharan)    #377: Spectral Clustering based on the graph p-Laplacian (Buehler, Hein)    #360: Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs (Radovanovic, Nanopoulos, Ivanovic)
	1B - Applied Probabilistic Models	Room: B
	Chair: Florence d'Alche Buc
	#387: Unsupervised Hierarchical Modeling of Locomotion Styles (Pan, Torresani)    #58: Exploiting Sparse Markov and Covariance Structure in Multiresolution Models (Jin Choi, Chandrasekaran, Willsky)    #525: A Bayesian Approach to Protein Model Quality Assessment (Kamisetty, James Langmead)    #351: Multi-class image segmentation using Conditional Random Fields and Global Classification (Plath, Toussaint, Nakajima)    #283: GAODE and HAODE: Two Proposals based on AODE to Deal with Continuous Variables (Julia Flores, A. Gámez, M. Martínez, M. Puerta)
	1C - Exploration in Reinforcement Learning	Room: C
	Chair: Pascal Poupart
	#302: The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning (Diuk, Li, Leffler)    #332: Near-Bayesian Exploration in Polynomial Time (Zico Kolter, Ng)    #290: Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs (Szita, Lorincz)    #61: Dynamic Analysis of Multiagent Q-learning with e-greedy Exploration (Rodrigues Gomes, Kowalczyk)    #229: Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search (Heidrich-Meisner, Igel)
	1D - Online Learning	Room: D
	Chair: Claudio Gentile
	#380: A simpler unified analysis of Budget Perceptrons (Sutskever)    #75: Efficient learning algorithms for changing environments (Hazan, Seshadhri)     #472: Online Learning by Ellipsoid Method (Yang, Jin, Ye)    #548: Learning Prediction Suffix Trees with Winnow (Karampatziakis, Kozen)    #42: Identifying Suspicious URLs: An Application of Large-Scale Online Learning (Ma, K. Saul, Savage, M. Voelker)
	1E - Ranking	Room: E
	Chair: Marie desJardins
	#498: BoltzRank: Learning to Maximize Expected Ranking Gain (Volkovs, Zemel)    #241: Decision Tree and Instance-Based Learning for Label Ranking (Cheng, Huehn, Huellermeier)    #163: Ranking with Ordered Weighted Pairwise Classification (Usunier, Buffoni, Gallinari)    #79: Ranking Interesting Subgroups (Rueping)    #101: Generalization Analysis of Listwise Learning-to-Rank Algorithms (Lan, Liu, Ma, Li)
12:20PM - 2:00PM	Lunch break
2:00PM - 4:00PM	2A - Graphs and Embeddings	Room: A
	Chair: Kilian Weinberger
	#506: Fitting a Graph to Vector Data (Daitch, Kelner, Spielman)    #418: Structure Preserving Embedding (Shaw, Jebara)    #188: Graph Construction and b-Matching for Semi-Supervised Learning (Jebara, Wang,, Chang)    #529: Partial Order Embedding with Multiple Kernels (McFee, Lanckriet)    #34: Probabilistic Dyadic Data Analysis with Local and Global Consistency (Cai, Wang, He)
	2B - Gaussian Processes	Room: B
	Chair: John Guiver
	#384: Non-linear Matrix Factorization with Gaussian Processes (D. Lawrence, Urtasun)    #344: Analytic Moment-based Gaussian Process Filtering (Peter Deisenroth, F. Huber, D. Hanebeck)    #323: Function factorization using warped Gaussian processes (N. Schmidt)    #255: Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities (Adams, Murray, MacKay)    #451: Large-scale Collaborative Prediction Using a Nonparametric Random Effects Model (Yu, Lafferty, Zhu)
	2C - Dynamical Systems	Room: C
	Chair: Doina Precup
	#538: Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems (Song, Huang, Smola, Fukumizu)    #295: Learning Nonlinear Dynamic Models (Langford, Salakhutdinov,, Zhang)    #443: Optimized Expected Information Gain for Nonlinear Dynamical Systems (Giovanni Busetto, Soon Ong, M. Buhmann)    #481: Learning Linear Dynamical Systems without Sequence Information (Huang, Schneider)    #478: Dynamic Mixed Membership Block Model for Evolving Networks (Fu, Song, Xing)
	2D - Kernels	Room: D
	Chair: Dale Schuurmans
	#542: Route Kernels for Trees (Aiolli, Da San Martino, Sperduti)    #485: The graphlet spectrum (Kondor, Shervashidze, Borgwardt)    #422: Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples (Zhou, Sun, Li)    #140: Non-Monotonic Feature Selection (Xu, Jin, Ye, R. Lyu, King)    #279: Regression by dependence minimization and its application to causal inference (Mooij, Janzing, Peters, Schoelkopf)
	2E - Learning Codebooks and Dictionaries	Room: E
	Chair: Samy Bengio
	#115: Gradient Descent with Sparsification: an iterative algorithm for sparse recovery with restricted isometry property (Garg, Khandekar)    #137: Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis (Cho, Saul)    #364: Online Dictionary Learning for Sparse Coding (Mairal, Bach, Ponce, Sapiro)    #523: Learning Non-Redundant Codebooks for Classifying Complex Objects (Zhang, Surve, Fern, Dietterich)    #198: Prototype Vector Machine for Large Scale Semi-supervised Learning (Zhang, T. Kwok,, Parvin)
4:00PM - 4:30PM	Coffee break
4:30PM - 5:50PM	Invited Talk: Yoav Freund	Leacock 132
	Drifting games, boosting and online learning
6:45PM - 11:00PM	Banquet at Montreal Science Centre

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Tuesday, June 16th

8:30AM - 9:50AM	Invited Talk: Corinna Cortes	Leacock 132
	Can learning kernels help performance?
9:50AM - 10:20AM	Coffee break
10:20AM - 12:20PM	3A - Clustering Algorithms	Room: A
	Chair: Tony Jebara
	#362: Multi-Assignment Clustering for Boolean Data (Peter Streich, Frank, Basin, M. Buhmann)    #388: K-means in Space: A Radiation Sensitivity Evaluation (Wagstaff, Bornstein)    #10: Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary? (Vinh Nguyen, Epps, Bailey)    #245: Fast Evolutionary Maximum Margin Clustering (Gieseke, Pahikkala, Kramer)    #179: Discriminative $k$ metrics (Szlam, Sapiro)
	3B - Inference in Probabilistic Models	Room: B
	Chair: Kevin Murphy
	#543: Orbit-Product Representation and Correction of Gaussian Belief Propagation (Johnson, Chernyak, Chertkov)    #296: Convex Variational Bayesian Inference for Large Scale Generalized Linear Models (Nickisch, Seeger)    #258: Archipelago: Nonparametric Bayesian Semi-Supervised Learning (Adams, Ghahramani)    #536: The Bayesian Group-Lasso for Analyzing Contingency Tables (Raman, Fuchs, Wild, Dahl, Roth)    #573: Split Variational Inference (Bouchard, Zoeter)
	3C - Reinforcement Learning with Temporal Differences	Room: C
	Chair: Shimon Whiteson
	#211: Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks (Makino)    #439: Regularization and Feature Selection in Least Squares Temporal-Difference Learning (Zico Kolter, Ng)    #546: Fast gradient-descent methods for temporal-difference learning with linear function approximation (S. Sutton, Reza Maei, Precup, Bhatnagar, Silver, Szepesvari, Wiewiora)    #467: Kernelized Value Function Approximation for Reinforcement Learning (Taylor, Parr)    #340: Constraint Relaxation in Approximate Linear Programs (Petrik, Zilberstein)
	3D - Structured Learning	Room: D
	Chair: Lawrence Carin
	#298: Large Margin Training for Hidden Markov Models with Partially Observed States (Do, Artieres)    #189: Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models (Cheng, Sha, Saul)    #297: Unsupervised Search-based Structured Prediction (Daume III)    #62: Sparse Higher Order Conditional Random Fields for improved sequence labeling (Qian, Jiang, Zhang, Huang,, Wu)    #503: Detecting the Direction of Causal Time Series (Peters, Janzing, Gretton, Schoelkopf)
	3E - Topic Models	Room: E
	Chair: Tom Dietterich
	#356: Evaluation Methods for Topic Models (Wallach, Murray, Salakhutdinov,, Mimno)    #162: Accounting for Burstiness in Topic Models (Doyle, Elkan)    #379: Topic-Link LDA: Joint Models of Topic and Author Community (Liu, Niculescu-Mizil,, Gryc)    #394: MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification (Zhu, Ahmed, Xing)    #469: Independent Factor Topic Models (Putthividhya, Attias, Nagarajan)
	3F - Transfer Learning and MultiTask Learning	Room: F
	Chair: Kai Yu
	#561: Deep Transfer via Second-Order Markov Logic (Davis, Domingos)    #407: Feature Hashing for Large Scale Multitask Learning (Weinberger, Dasgupta, Attenberg, Langford, Smola)    #341: A Convex Formulation for Learning Shared Structures from Multiple Tasks (Chen, Tang, Liu, Ye)    #141: EigenTransfer: A Unified Framework for Transfer Learning (Dai, Jin, Xue, Yang, Yu)    #445: Domain Adaptation from Multiple Sources via Auxiliary Classifiers (Duan, W. Tsang, Xu,, Chua)
12:20PM - 2:00PM	Lunch break
2:00PM - 4:00PM	4A - Weak Supervision	Room: A
	Chair: Fei Sha
	#563: Semi-Supervised Learning Using Label Mean (Li, T. Kwok, Zhou)    #313: Partially Supervised Feature Selection with Regularized Linear Models (Helleputte, Dupont)    #578: Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-supervised Learning (Xu, White, Schuurmans)    #96: Supervised Learning from Multiple Experts: Whom to trust when everyone lies a bit (Raykar, Yu, Zhao, Jerebko, Florin, Valadez, Bogoni, Moy)    #259: Good Learners for Evil Teachers (Dekel, Shamir)
	4B - Learning Structures	Room: B
	Chair: Pedro Domingos
	#355: Structure learning with independent non-identically distributed data (Tillman)    #246: Structure Learning of Bayesian Networks using Constraints (P. de Campos, Zeng, Ji)    #276: Learning structurally consistent undirected probabilistic graphical models (Roy, Lane, Werner-Washburne)    #284: Sparse Gaussian Graphical Models with Unknown Block Structure (Marlin, Murphy)    #576: Learning Markov Logic Network Structure via Hypergraph Lifting (Kok, Domingos)
	4C - Active Learning	Room: C
	Chair: Yoav Freund
	#492: Learning to Segment from a Few Well-Selected Training Images (Alireza Farhangfar, Greiner,, Szepesvari)    #392: Importance Weighted Active Learning (Beygelzimer, Dasgupta, Langford)    #393: Learning from Measurements in Exponential Families (Liang, I. Jordan,, Klein)    #427: Online Feature Elicitation in Interactive Optimization (Boutilier, Regan, Viappiani)    #582: Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision (Sindhwani, Melville, Lawrence)
	4D - Lassos and other L1s	Room: D
	Chair: Volker Roth
	#262: Stochastic Methods for L1 Regularized Loss Minimization (Shalev-Shwartz, Tewari)    #168: Blockwise Coordinate Descent Procedures for the Multi-task Lasso, with Applications to Neural Semantic Basis Discovery (Liu, Palatucci, Zhang)    #475: An Efficient Projection for L1,Infinity Regularization (Quattoni, Carreras, Collins, Darrell)    #151: An Accelerated Gradient Method for Trace Norm Minimization (Ji, Ye)    #471: Group Lasso with Overlaps and Graph Lasso (Jacob, Obozinski, Vert)
	4E - Document Collections	Room: E
	Chair: Johannes Fuernkranz
	#328: Bayesian Clustering for Email Campaign Detection (Haider, Scheffer)    #184: A Novel Lexicalized HMM-based Learning Framework for Web Opinion Mining (Jin, Hay Ho)    #281: Learning Spectral Graph Transformations for Link Prediction (Kunegis, Lommatzsch)    #346: Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem (Yue, Joachims)    #210: Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model (Li, Yang, Xue)
	4F - Food for Thought	Room: F
	Chairs: Leon Bottou and Michael Littman
	#119: Curriculum Learning (Bengio, Louradour, Collobert, Weston)    #447: Herding Dynamical Weights to Learn (Welling)    #497: Sequential Bayesian Prediction in the Presence of Changepoints (Garnett, Osborne, Roberts)    #322: Model-Free Reinforcement Learning as Mixture Learning (Vlassis, Toussaint)    #508: Active Learning for Directed Exploration of Complex Systems (Burl, Wang)
4:00PM - 4:30PM	Coffee break
4:30PM - 5:30PM	Awards session	Leacock 132
6:45PM - 11:00PM	Poster session: Papers from sessions 1A to 3F	Leacock/Arts

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Wednesday, June 17th

8:30AM - 9:50AM	Invited Talk: Emmanuel Dupoux	Leacock 132
	How do infants bootstrap into spoken language?: Models and challenges
9:50AM - 10:20AM	Coffee break
10:20AM - 12:20PM	5A - Algorithms	Room: A
	Chair: Alex Smola
	#511: Proximal regularization for online and batch learning (Do, Le, Foo)    #20: A majorization-minimization algorithm for (multiple) hyperparameter learning (Foo, Do, Ng)    #315: A Least Squares Formulation for a Class of Generalized Eigenvalue Problems in Machine Learning (Sun, Ji, Ye)    #256: On Sampling-based Approximate Spectral Decomposition (Kumar, Mohri, Talwalkar)    #123: Efficient Euclidean Projections in Linear Time (Liu, Ye)
	5B - Priors in Probabilistic Models	Room: B
	Chair: Max Welling
	#347: Bayesian inference for Plackett-Luce ranking models (Guiver, Snelson)    #390: Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors (Andrzejewski, Zhu, Craven)    #267: Nonparametric Factor Analysis with Beta Process Priors (Paisley, Carin)    #270: Accelerated Gibbs Sampling for the Indian Buffet Process (Doshi-Velez, Ghahramani)    #319: A Stochastic Memoizer for Sequence Data (Wood, Archambeau, Gasthaus, James, Whye Teh)
	5C - Reinforcement Learning in High Order Environments	Room: C
	Chair: Kurt Driessens
	#532: Binary Action Search for Learning Continuous-Action Control Policies (Pazis, Lagoudakis)    #446: Predictive Representations for Policy Gradient in POMDPs (Boularias, Chaib-draa)    #556: Stochastic Search using the Natural Gradient (Yi, Wierstra, Schaul,, Schmidhuber)    #90: Approximate Inference for Planning in Stochastic Relational Worlds (Lang, Toussaint)    #376: Discovering Options from Example Trajectories (Zang, Zhou, Minnen, Isbell)
	5D - Learning Theory	Room: D
	Chair: Sham Kakade
	#309: Nonparametric Estimation of the Precision-Recall Curve (Clemencon, Vayatis)    #400: Surrogate Regret Bounds for Proper Losses (Reid, Williamson)    #289: Robust Bounds for Classification via Selective Sampling (Cesa-Bianchi, Gentile, Orabona)    #89: PAC-Bayesian Learning of Linear Classifiers (Germain, Lacasse, Laviolette, Marchand)    #367: Piecewise-stationary bandit problems with side observations (Yuan Yu, Mannor)
	5E - Paul Utgoff Memorial Session: Learning for Discrete Problems	Room: E
	Chair: Carla Brodley
	#494: Bandit-Based Optimization on Graphs with Application to Library Performance Tuning (de Mesmay, Rimmel, Voronenko, Püschel)    #21: Robust Feature Extraction via Information Theoretic Learning (Yuan, Hu)    #366: Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties (Kuzelka, Zelezny)    #203: Rule Learning with Monotonicity Constraints (Kotlowski, Slowinski)    #539: Grammatical Inference as a Principal Component Analysis Problem (Bailly, Denis, Ralaivola)
12:20PM - 2:00PM	Lunch break
2:00PM - 4:00PM	6A - Structured Learning: Metric Learning	Room: A
	Chair: Sofus Attila Macskassy
	#175: Polyhedral Outer Approximations with Application to Natural Language Parsing (Martins, Smith, Xing)    #274: On Primal and Dual Sparsity of Markov Networks (Zhu, Xing)    #420: Learning Structural SVMs with Latent Variables (Yu, Joachims)    #46: An Efficient Sparse Metric Learning in High-Dimensional Space via $\ell_1$-Penalized Log-Determinant Regularization (Qi, Tang, Chua, Zhang)    #232: Learning Instance Specific Distances Using Metric Propagation (Zhan, Li, Li, Zhou)
	6B - Deep Architectures	Room: B
	Chair: Yann LeCun
	#571: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (Lee, Grosse, Ranganath, Ng)    #363: Using Fast Weights to Improve Persistent Contrastive Divergence (Tieleman, Hinton)    #218: Large-scale Deep Unsupervised Learning using Graphics Processors (Raina, Madhavan, Ng)    #178: Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style (Taylor, Hinton)    #223: Deep Learning from Temporal Coherence in Video (Mobahi, Collobert, Weston)
	6C - Learning Actions and Sequences	Room: C
	Chair: Nikos Vlassis
	#271: Robot Trajectory Optimization using Approximate Inference (Toussaint)    #311: Trajectory Prediction: Learning to Map Situations to Robot Trajectories (Jetchev, Toussaint)    #516: Learning Complex Motions by Sequencing Simpler Motion Templates (Neumann, Maass, Peters)    #421: Learning When to Stop Thinking and Do Something! (Poczos, Abbasi-Yadkori, Szepesvari, Greiner,, Sturtevant)    #500: Monte-Carlo Simulation Balancing (Silver, Tesauro)
	6D - Learning Kernels	Room: D
	Chair: Francis Bach
	#149: More Generality in Efficient Multiple Kernel Learning (Varma, Rakesh Babu)    #520: Multiple Indefinite Kernel Learning with Mixed Norm Regularization (Kowalski, Szafranski, Ralaivola)    #399: Learning Kernels from Indefinite Similarities (Chen, Gupta, Recht)    #505: SimpleNPKL:Simple NonParametric Kernel Learning (Zhuang, Tsang, Hoi)    #193: Geometry-aware Metric Learning (Lu, Jain, Dhillon)
	6E - Boosting	Room: E
	Chair: Shai Shalev-Schwartz
	#231: Boosting products of base classifiers (Kegl, Busa-Fekete>)    #417: ABC-Boost: Adaptive Base Class Boost for Multi-class Classification (Li)    #459: Compositional Noisy-Logical Learning (Yuille, Zheng)    #146: Boosting with Structural Sparsity (Duchi, Singer)    #452: Learning with Structured Sparsity (Huang, Zhang, Metaxas)
4:00PM - 4:30PM	Coffee break
4:30PM - 5:30PM	ICML business meeting	Leacock 132
6:45PM - 11:00PM	Poster Session: Papers from sessions 4A to 6F	Leacock/Arts