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UAI 2009 Schedule

Friday, June 19

08:45 - 09:00 Opening Comments
09:00 - 10:00Keynote talk: Yoshua Bengio, University of Montreal
Scaling up graphical models (Chair: Kevin Murphy)
10:00 - 10:25Exact Structure Discovery in Bayesian Networks with Less Space
Pekka Parviainen, Mikko Koivisto
10:25 - 10:50Distributed Parallel Inference on Large Factor Graphs
Joseph Gonzalez, Yucheng Low, Carlos Guestrin, David O'Hallaron
10:50 - 11:2030 minute break
Learning & Estimation I (Chair: Kevin Murphy)
11:20 - 11:45Conditional Probability Tree Estimation Analysis and Algorithms
Alina Beygelzimer, John Langford, Yury Lifshits, Gregory Sorkin, Alex Strehl
11:45 - 12:10Robust Graphical Modelling with t-Distributions
Michael Finegold, Mathias Drton
12:10 - 12:35Interpretation and Generalization of Score Matching
Siwei Lyu
12:35 - 13:00Alternating Projections for Learning with Expectation Constraints
Kedar Bellare, Gregory Druck, Andrew McCallum
13:00 - 14:00Lunch, 1.5 hours, AUAI chairs meeting
Reinforcement Learning (Chair: Nando de Frietas)
14:30 - 14:55 Regret-based Reward Elicitation for Markov Decision Processes
Kevin Regan, Craig Boutilier
14:55 - 15:20 A Bayesian Sampling Approach to Exploration in Reinforcement Learning
Michael Littman, Lihong Li, Ali Nouri, David Wingate, John Asmuth
15:20 - 15:45 New inference strategies for solving Markov Decision Processes using reversible jump MCMC
Matt Hoffman, Hendrik Kueck, Nando de Freitas, Arnaud Doucet
15:45 - 16:10 Censored Exploration and the Dark Pool Problem
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, Jennifer Wortman
16:10 - 16:4030 minute break
Programming Formalisms (Chair: Nando de Frietas)
16:40 - 17:05 Monolingual Probabilistic Programming Using Generalized Coroutines
Oleg Kiselyov, Chung-chieh Shan
17:05 - 17:30 First-Order Mixed Integer Linear Programming
Geoffrey Gordon, Sue Ann Hong, Miroslav Dudik
17:30 - 18:16 Poster spotlights (Chair: Jeff Bilmes)
(23 posters, 2 minutes each)
18:16 - 20:15 Dinner, 2 hours, on your own.
20:15 - 10:45 Poster session I of II, 2.5 hours.

Saturday, June 20

Learning & Estimation II (Chair: Nir Friedman)
08:45 - 09:10 Bayesian Multitask Learning with Latent Hierarchies
Hal Daume
09:10 - 09:35 Improving Compressed Counting
Ping Li
09:35 - 10:00 Ordinal Boltzmann Machines for Collaborative Filtering
The Truyen Tran, Dinh Phung, Svetha Venkatesh
10:00 - 10:25Virtual Vector Machine for Bayesian Online Classification
Thomas Minka, Rongjing Xiang, Alan Qi
10:25 - 10:5530 minute break
Clustering (Chair: Nir Friedman)
10:55 - 11:20 Quantum Annealing for Clustering
Kenichi Kurihara, Shu Tanaka, Seiji Miyashita
11:20 - 11:45 A Uniqueness Theorem for Clustering
Reza Bosagh Zadeh, Shai Ben-David
11:45 - 12:31 Poster spotlights (Chair: Andrew Ng) (23 posters, 2 minutes each)
12:31 - 14:00 Lunch, 1.5 hours, on your own
14:00 - 16:30 Poster session II of II, 2.5 hours
16:40 - 17:10 Buses to UAI/COLT joint banquet
17:10 - 21:30 Tour of Fort Chambly, followed by banquet

Sunday, June 21

09:00 - 10:00 Keynote talk: James Robins, Harvard University
Causality I (Chair: Vince Conitzer)
10:00 - 10:25 Effects of Treatment on the Treated: Identification and Generalization
Ilya Shpitser, Judea Pearl
10:25 - 10:50 Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
Mark Schmidt, Kevin Murphy
10:50 - 11:2030 minute break
Causality II & Graphical Models (Chair: Vince Conitzer)
11:20 - 11:45 On the Identifiability of the Post-Nonlinear Causal Model
Kun Zhang, Aapo Hyvärinen
11:45 - 12:10 A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio
12:10 - 12:35 Convergent message passing algorithms - a unifying view
Talya Meltzer, Amir Globerson, Yair Weiss
12:35 - 13:00 A factorization criterion for acyclic directed mixed graphs
Thomas Richardson
13:00 - 14:301.5 hour lunch
Temporal models (Chair: Thomas Richardson)
14:30 - 14:55 Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Ido Cohn, Tal El-hay, Nir Friedman, Raz Kupferman
14:55 - 15:20 Learning Continuous-Time Social Network Dynamics
Yu Fan, Christian Shelton
15:20 - 15:45 Products of Hidden Markov Models: It Takes N>1 to Tango
Graham Taylor, Geoffrey Hinton
15:45 - 16:1530 minute break
Games and Decisions (Chair: Thomas Richardson)
16:15 - 16:40 Temporal Action-Graph Games: A New Representation for Dynamic Games
Albert Xin Jiang, Kevin Leyton-Brown, Avi Pfeffer
16:40 - 17:05 Measuring Inconsistency in Probabilistic Knowledge Bases
Matthias Thimm
17:05 - 17:30 Prediction Markets, Mechanism Design, and Cooperative Game Theory
Vincent Conitzer
17:30 - 18:30Business meeting

Poster Session I of II: 23 papers

01. Temporal Difference Networks for Dynamical Systems with Continuous Observations and Actions
     Vigorito Christopher
02. Quantum Annealing for Variational Bayes Inference
     Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa , Seiji Miyashita
03. Exploring compact reinforcement-learning representations with linear regression
     Thomas Walsh, Istvan Szita, Carlos Diuk, Michael Littman
04. MAP Estimation, Message Passing, and Perfect Graphs
     Tony Jebara
05. Deterministic POMDPs Revisited
     Blai Bonet
06. Convexifying the Bethe Free Energy
     Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman
07. Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions
     Benjamin Lubin, David Parkes
08. Which Spatial Partition Trees are Adaptive to Intrinsic Dimension?
     Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta
09. A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form Games
     Miroslav Dudik, Geoffrey Gordon
10. Characterizing predictable classes of processes
     Daniil Ryabko
11. Convex Coding
     David Bradley, J. Andrew Bagnell
12. Counting Belief Propagation
     Kristian Kersting, Babak Ahmadi, Sriraam Natarajan
13. L_2 Regularization for Learning Kernels
     Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
14. Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
     Fabio Cozman, Rodrigo Polastro
15. Domain Knowledge Uncertainty and Probabilistic Parameter Constraints
     Yi Mao, Guy Lebanon
16. Improved Mean and Variance Approximations for Belief Net Response via Network Doubling
     Peter Hooper, Yasin Abbasi-Yadkori, Bret Hoehn, Russell Greiner
17. BPR: Bayesian Personalized Ranking from Implicit Feedback
     Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme
18. Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence
     Mathias Niepert
19. Multilingual Topic Models for Unaligned Text
     Jordan Boyd-Graber, David Blei
20. Bayesian Discovery of Linear Acyclic Causal Models
     Patrik Hoyer, Antti Hyttinen
21. The Infinite Latent Events Model
     David Wingate, Noah Goodman, Daniel Roy, Josh Tenenbaum
22. Identifying confounders using additive noise models
     Dominik Janzing, Jonas Peters, Joris Mooij, Bernhard Schoelkopf
23. Using hierarchical information in predicting gene function
     Sara Mostafavi, Quaid Morris

Poster Session II of II: 23 papers

01. On Maximum a Posteriori Estimation of Hidden Markov Processes
     Armen Allahverdyan, Aram Galstyan
02. Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal Decision Making
     Mark Crowley, David Poole, John Nelson
03. Optimization of Structured Mean Field Objectives
     Alexandre Bouchard-Côté, Mike Jordan
04. REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
     Peter Bartlett, Ambuj Tewari
05. Approximate inference on planar graphs using Loop Calculus and Belief Propagation
     Vicenc Gomez, Bert Kappen, Misha Chertkov
06. Generating Optimal Plans in Highly-Dynamic Domains
     Christian Fritz, Sheila McIlraith
07. MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
     M. Pawan Kumar, Daphne Koller
08. Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies
     Yevgeniy Vorobeychik
09. Herding Dynamic Weights for Partially Observed Random Field Models
     Max Welling
10. Probabilistic Structured Predictors
     Shankar Vembu, Thomas Gärtner, Mario Boley
11. Multi-Task Feature Learning Via Efficient L2,1-Norm Minimization
     Jun Liu, Shuiwang Ji, Jieping Ye
12. Bisimulation-based Approximate Lifted Inference
     Prithviraj Sen, Amol Deshpande, Lise Getoor
13. Multiple Source Adaptation and the Renyi Divergence
     Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
14. Constraint Processing in Lifted Probabilistic Inference
     Jacek Kisynski, David Poole
15. Group Sparse Priors for Covariance Estimation
     Benjamin Marlin, Mark Schmidt, Kevin Murphy
16. Lower Bound Bayesian Networks - Efficient Inference of Lower Bounds on Probability Distributions
     Daniel Andrade, Bernhard Sick
17. A Bayesian Framework for Community Detection Integrating Content and Link
     Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
18. Most Relevant Explanation: Properties, Algorithms, and Evaluations
     Changhe Yuan, Xiaolu Liu, Tsai-Ching Lu, Heejin Lim
19. On Smoothing and Inference for Topic Models
     Arthur Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh
20. Computing Posterior Probabilities of Structural Features in Bayesian Networks
     Jin Tian, Ru He
21. Correlated Non-Parametric Latent Feature Models
     Finale Doshi-Velez, Zoubin Ghahramani
22. The Temporal Logic of Causal Structures
     Samantha Kleinberg, Bud Mishra
23. The Entire Quantile Path of a Risk-Agnostic SVM Classifier
     Jin Yu, S V N Vishwanathan, Jian Zhang