Overview: The Applications Workshop and tutorials will be on July 19th. The main conference will run from the morning of July 20 through late afternoon/early evening on Sunday, July 22.
We accepted 57 out of 180 submissions.
Note that all papers appear as full length papers in the UAI proceedings. The only difference between poster and plenary papers is the type of presentation at the conference.
Program
Thursday, July 19
- 08:30 - 17:30 Tutorial Program
- 19:00 - 22:00 Reception in the lobby of the Forest Sciences Centre
Friday, July 20 - Room 1005 Forest Sciences Centre
- 08:45 - 09:00 Welcome
- 09:00 - 10:40 Session I: Preferences and Rankings
Chair: Kevin Leyton-Brown- Minimax Regret Based Elicitation of Generalized Additive Utilities, Darius Braziunas and Craig Boutilier.
- More-or-Less CP-Networks, Fusun Yaman and Marie desJardins.
- A Utility Framework for Bounded-Loss Market Makers, Yiling Chen and David Pennock.
- Ranking Under Uncertainty, Or Zuk, Liat Ein-Dor, and Eytan Domany.
- 10:40 - 11:10 Break
- 11:10 - 12:10 Invited talk I: Moises Goldszmidt (Microsoft Research) Making Life Better one Large Distributed System at a Time: Challenges for UAI Research
- 12:10 - 13:45 Lunch (Private Chairs Meeting)
- 13:45 - 15:00 Session II: Inference
Chair: Prakash Shenoy- Studies in Lower Bounding Probability of Evidence using the Markov Inequality, Vibhav Gogate, Bozhena Bidyuk and Rina Dechter.
- Large-Flip Importance Sampling, Firas Hamze and Nando de Freitas.
- Reasoning at the Right Time Granularity, Suchi Saria, Uri Nodelman and Daphne Koller.
- 15:00 - 15:30 Break
- 15:30 - 16:20 Session III: Learning
Chair: David McAllester- Statistical Translation, Heat Kernels, and Expected Distance, Joshua Dillon, Yi Mao, Guy Lebanon and Jian Zhang.
- Bayesian Active Distance Metric Learning, Liu Yang, Rong Jin and Rahul Sukthankar.
- 16:20 - 16:55 Poster highlights
- 16:55 - 19:00 Poster session I
- Optimizing Memory-Bounded Controllers for Decentralized POMDPs, Christopher Amato, Daniel S. Bernstein and Shlomo Zilberstein.
- Generalized Polya Urn for Time-varying Dirichlet Process Mixtures, Francois Caron, Manuel Davy and Arnaud Doucet.
- Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks, Arthur Choi, Mark Chavira and Adnan Darwiche.
- Causal Reasoning in Graphical Time Series Models, Michael Eichler and Vanessa Didelez.
- Search for Choquet-Optimal Paths under Uncertainty, Lucie Galand and Patrice Perny.
- Fast Nonparametric Conditional Density Estimation, Michael Holmes, Alexander Gray and Charles Isbell.
- Learning Bayesian Network Structure from Correlation-Immune Data, Eric Lantz, Soumya Ray and David Page.
- Best-First AND/OR Search for Most Probable Explanations, Radu Marinescu and Rina Dechter.
- AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models, Robert Mateescu and Rina Dechter.
- Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods, Gergely Neu and Csaba Szepesvari.
- Reading Dependencies from Polytree-Like Bayesian Networks, Jose Pena.
- Causal Bounds and Instruments, Roland Ramsahai.
- A Tractable Approach to Finding Closest Truncated-Commute-Time Neighbors in Large Graphs, Purnamrita Sarkar and Andrew Moore.
- Markov Logic in Infinite Domains, Parag Singla and Pedro Domingos.
- Constrained Automated Mechanism Design for Infinite Games of Incomplete Information, Yevgeniy Vorobeychik, Daniel Reeves and Michael Wellman.
- Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification, Brian Ziebart, Anind Dey and J. Andrew Bagnell.
Saturday, July 21 - Room 1005 Forest Sciences Centre
- 09:00 - 10:40 Session IV: Learning
Chair: David Heckerman- A New Parameter Learning Method for Bayesian Networks with Qualitative Influences, Ad Feelders.
- Bayesian Structure Learning Using Dynamic Programming and MCMC, Daniel Eaton and Kevin Murphy.
- On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter, Tomi Silander, Petri Kontkanen, and Petri Myllymäki.
- On Discarding, Caching and Recalling Samples in Active Learning, Ashish Kapoor and Eric Horvitz.
- 10:40 - 11:10 Break
- 11:10 - 12:10 Invited talk II: James E. Smith, (The Fuqua School of Business, Duke University), The Optimizer's Curse
- 12:10 - 13:45 Lunch (Open Business meeting in ICICS/CS Room X735)
- 13:45 - 15:00 Session V: Decision Theory and Planning
Chair: Fahiem Bacchus- Imitation Learning with a Value-Based Prior, Umar Syed and Robert Schapire.
- Learning Probabilistic Relational Dynamics for Multiple Tasks, Ashwin Deshpande, Brian Milch, Luke Zettlemoyer and Leslie Kaelbling.
- Reachabillity Under Uncertainty, Allen Chang and Eyal Amir.
- 15:00 - 15:30 Break
- 15:30 - 16:20 Session VI: Theory
Chair: Thomas Richardson- A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables, Jiji Zhang.
- What Counterfactuals Can Be Tested, Ilya Shpitser and Judea Pearl.
- 16:20 - 16:55 Poster highlights
- 16:55 - 19:00 Poster session II
- Evaluating Influence Diagrams with Decision Circuits, Debarun Bhattacharjya and Ross Shachter.
- Bandit Algorithms for Tree Search, Pierre-Arnaud Coquelin and Rémi Munos.
- Shift-Invariant Sparse Coding for Audio Classification, Roger Grosse, Rajat Raina, Helen Kwong and Andrew Ng.
- Analysis of Semi-Supervised Learning with the Yarowsky Algorithm, Gholamreza Haffari and Anoop Sarkar.
- Template Based Inference in Symmetric Relational Markov Random Fields, Ariel Jaimovich, Ofer Meshi and Nir Friedman.
- Polynomial Constraints in Causal Bayesian Networks, Changsung Kang and Jin Tian.
- Evaluation of the Causal Effect of Control Plans in Non-Recursive Structural Equation Models, Manabu Kuroki and Zhihong Cai.
- Nonparametric Bayes Pachinko Allocation, Wei Li, David Blei, and Andrew McCallum.
- Consensus Ranking Under the Exponential Model, Marina Meila, Kapil Phadnis, Arthur Patterson and Jeff Bilmes.
- Mixture-of-Parents Maximum Entropy Markov Models, David Rosenberg, Dan Klein, and Ben Taskar.
- Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs, Sven Seuken and Shlomo Zilberstein.
- Improved Dynamic Schedules for Belief Propagation, Charles Sutton and Andrew McCallum.
- A Criterion for Parameter Identification in Structural Equation Models, Jin Tian.
- Policy Iteration for Relational MDPs, Chenggang Wang and Roni Khardon.
- Importance Sampling via Variational Optimization, Ydo Wexler and Dan Geiger.
Sunday, July 22 - Room 1005 Forest Sciences Centre
- 09:00 - 10:40 Session VII: Inference
Chair: Ilya Shpitser- Accuracy Bounds for Belief Propagation, Alexander Ihler.
- Convergent Propagation Algorithms with Oriented Trees, Amir Globerson and Tommi Jaakkola.
- Survey Propagation Revisited: An Empirical Study, Lukas Kroc, Ashish Sabharwal and Bart Selman.
- MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies, Yair Weiss, Chen Yanover and Talya Meltzer.
- 10:40 - 11:10 Break
- 11:10 - 12:10 Invited talk III: Marco F. Ramoni (Harvard Medical School and Massachusetts Institute of Technology) Statistical Mechanics of Biological Networks
- 12:10 - 14:00 Lunch
- 14:00 - 15:00 Invited talk IV: James V. Zidek (Department of Statistics, University of British Columbia) Computational Strategies for Modeling and Regulating Air Pollution Fields
- 15:00 - 15:30 Break
- 15:30 - 17:10 Session VIII: Applications
Chair: Petri Myllymäki- A Probabilistic Model for Anomaly Detection in Remote Sensor Data Streams, Ethan Dereszynski and Thomas Dietterich.
- Discovering Patterns in Biological Sequences by Optimal Segmentation, Joseph Bockhorst and Nebojsa Jojic.
- Determining the Number of Non-spurious Arcs in a Learned DAG Model, Jennifer Listgarten and David Heckerman.
- Collaborative Filtering and the Missing at Random Assumption, Benjamin Marlin, Richard Zemel, Sam Roweis and Malcolm Slaney.
- 17:15 Conference ends