UAI 2009 Schedule
Friday, June 19
| 08:45 - 09:00 | Opening Comments | |
| 09:00 - 10:00 | Keynote talk: Yoshua Bengio, University of Montreal | |
| Scaling up graphical models (Chair: Kevin Murphy) | ||
| 10:00 - 10:25 | Exact Structure Discovery in Bayesian Networks with Less Space | |
| Pekka Parviainen, Mikko Koivisto | ||
| 10:25 - 10:50 | Distributed Parallel Inference on Large Factor Graphs | |
| Joseph Gonzalez, Yucheng Low, Carlos Guestrin, David O'Hallaron | ||
| 10:50 - 11:20 | 30 minute break | |
| Learning & Estimation I (Chair: Kevin Murphy) | ||
| 11:20 - 11:45 | Conditional Probability Tree Estimation Analysis and Algorithms | |
| Alina Beygelzimer, John Langford, Yury Lifshits, Gregory Sorkin, Alex Strehl | ||
| 11:45 - 12:10 | Robust Graphical Modelling with t-Distributions | |
| Michael Finegold, Mathias Drton | ||
| 12:10 - 12:35 | Interpretation and Generalization of Score Matching | |
| Siwei Lyu | ||
| 12:35 - 13:00 | Alternating Projections for Learning with Expectation Constraints | |
| Kedar Bellare, Gregory Druck, Andrew McCallum | ||
| 13:00 - 14:00 | Lunch, 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 - 16:40 | 30 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 - 10:25 | Virtual Vector Machine for Bayesian Online Classification | |
| Thomas Minka, Rongjing Xiang, Alan Qi | ||
| 10:25 - 10:55 | 30 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 - 11:20 | 30 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 - 14:30 | 1.5 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 - 16:15 | 30 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 - 18:30 | Business 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
