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UAI 2010 Schedule

All plenary sessions are in the theatre. Each day starts with a continental breakfast (price included in your registration) in the lobby. The first poster session is in the ballroom, the second is in the theatre. The banquet is in the ballroom.

Thursday July 8th

08:30 Breakfast (in lobby)
09:30 - 11:30 Linear-algebraic methods for learning latent variable models
Sanjoy Dasgupta, University of California, San Diego
90 minute lunch, on your own
13:00 - 15:00 Learning and Reasoning With Incomplete Data: Foundations and Algorithms
Manfred Jaeger, Aalborg University
30 minute coffee break
15:30 - 17:30 Non-Gaussian methods for learning linear structural equation models
Shohei Shimizu and Yoshinobu Kawahara, Osaka University

Friday, July 9th

08:00 Breakfast (in lobby)
08:45 - 09:00 Opening Comments (Peter Grünwald and Peter Spirtes)
09:00 - 10:00 Invited Talk: The Wisdom of Crowds in the Aggregation of Rankings
Mark Steyvers, University of California, Irvine
LEARNING (session chair: Teemu Roos)
10:00 - 10:25 Semi-supervised Learning by Modeling Multiple-Annotator Expertise
Yan Yan, Romer Rosales, Glenn Fung, Jennifer Dy
10:25 - 10:50 Multi-Domain Collaborative Filtering
Yu Zhang, Bin Cao, Dit-Yan Yeung
10:50 - 11:15 Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Ryan Adams, George Dahl, Iain Murray
30 minute break
CAUSALITY (Ilya Shpitser)
11:45 - 12:10 Learning Why Things Change: The Difference-Based Causality Learner
Mark Voortman, Denver Dash, Marek Druzdzel
12:10 - 12:35 On Measurement Bias in Causal Inference
Judea Pearl
12:35 - 13:00 Causal Conclusions that Flip Repeatedly
Kevin Kelly, Conor Mayo-Wilson
75 minutes lunch, on your own. Also: AUAI chairs meeting
APPLICATIONS (David Heckerman)
14:15 - 14:40 ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards
Alan Carlin, Nathan Schurr, Janusz Marecki
14:40 - 15:05 Comparative Analysis of Graphical Models for User Activity Recognition with an Instrumented Walker
Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Tung, Allan Caine
15:05 - 15:30 Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream
Amr Ahmed, Eric Xing
15:30 - 15:55 Maximizing Spread of Cascades Using Network Design
Daniel Sheldon, Bistra Dilkina, Adam Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad,Carla Gomes, David Shmoys, Will Allen, Ole Amundsen, William Vaughan
15:55 - 16:30 Hybrid Generative/Discriminative Learning for Automatic Image Annotation
Shuang-Hong Yang, Jiang Bian, Hongyuan Zha
15 minute break
POSTERS (Peter & Peter)
16:45 - 17:30 Poster Spotlight I (28 posters, 90 seconds each)
2 hours dinner, on your own.
19:30 - 22:00 Poster session I of II. location: ballroom

Saturday, July 10

08:00 Breakfast (in lobby)
INFERENCE (Gal Elidan)
08:45 - 09:10 Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models
Nicholas Ruozzi, Sekhar Tatikonda
09:10 - 09:35 BEEM : Bucket Elimination with External Memory
Kalev Kask, Rina Dechter, Andrew Gelfand
09:35 - 10:00 Gibbs sampling in open-universe stochastic languages
Nimar Arora, Erik Sudderth, Rodrigo de Salvo Braz, Stuart Russell
10:00 - 10:25 Gaussian Process Structural Equation Models with Latent Variables
Ricardo Silva, Robert Gramacy
35 minute break
11:00 - 12:00 Invited Talk: Markovian (and conceivably causal) representations of stochastic processes
Cosma Shalizi, Carnegie Mellon University
12:00 - 12:25 Inferring deterministic causal relations (best student paper award)
Povilas Daniušis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
12:25 - 12:50 A Convex Formulation for Learning Task Relationships in Multi-Task Learning (best paper award)
Yu Zhang, Dit-Yan Yeung
70 minutes lunch, on your own
14:00 - 14:35 Report on UAI 2010 Approximate Inference Challenge/Evaluation
14:35 - 15:20 Poster spotlight II (30 posters, 90 seconds each)
15:20 - 17:45 Poster session II of II. location: theatre, note different location from Poster Session I
17:45 guests begin to arrive
18:30 - 21:00 dinner
(talk starts around 20:00)
UAI 2010 Banquet. location: ballroom
Banquet Speaker: David Stork, Ricoh Innovations
Computer vision and computer graphics in the study of fine art: New rigorous approaches to analyzing master paintings and drawings

Sunday, July 11

08:00 Breakfast (in lobby)
08:30 - 08:55 Understanding Sampling Style Adversarial Search Methods
Raghuram Ramanujan, Ashish Sabharwal, Bart Selman
08:55 - 09:20 Playing games against nature: optimal policies for renewable resource allocation
Stefano Ermon, Carla Gomes, Bart Selman
09:20 - 09:45 Automated Planning in Repeated Adversarial Games
Enrique Munoz de Cote, Adam M. Sykulski, Archie Chapman, Nick Jennings
09:45 - 10:10 Return Density Approximation for Reinforcement Learning
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka
20 minute break
10:30 - 11:30 Invited Talk: Probabilistic Graphical Models for Structural Biology
Christopher Langmead, Carnegie Mellon University
11:30 - 11:55 On the Validity of Covariate Adjustment for Estimating Causal Effects
Ilya Shpitser, Tyler Vander Weele
11:55 - 12:20 Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
Kun Zhang, Bernhard Schölkopf, Dominik Janzing
70 minutes lunch (on your own)
13:30 - 13:55 Matrix Coherence and the Nystrom Method
Ameet Talwalkar, Afshin Rostamizadeh
13:55 - 14:20 Parameter-Free Spectral Kernel Learning
Qi Mao, Ivor W. Tsang
14:20 - 14:45 A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models
Miika Pihlaja, Michael Gutmann, Aapo Hyvarinen
14:45 - 15:10 Robust Metric Learning with Smooth Optimization
Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu
20 minute break
15:30 - 15:55 Probabilistic Similarity Logic
Matthias Broecheler, Lilyana Mihalkova, Lise Getoor
15:55 - 16:20 Automatic Tuning of Interactive Perception Applications
Qian Zhu, Branislav Kveton, Lily Mummert, Padmanabhan Pillai
16:20 - 16:45 GraphLab: A New Framework for Parallel Machine Learning
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin
16:45 - 17:30 Business meeting

Poster Session I of II: 28 papers

  1. Source separation and higher-order causal analysis of MEG and EEG Kun Zhang, Aapo Hyvarinen
  2. The Cost of Troubleshooting Cost Clusters with Inside Information Thorsten Ottosen, Finn Jensen
  3. On a Class of Bias-Amplifying Variables that Endanger Effect Estimates Judea Pearl
  4. Solving Hybrid Influence Diagrams with Deterministic Variables Yijing Li, Prakash Shenoy
  5. Dynamic programming in influence diagrams with decision circuits Ross Shachter, Debarun Bhattacharjya
  6. Truthful Feedback for Sanctioning Reputation Mechanisms Jens Witkowski
  7. MDPs with Unawareness Joseph Y. Halpern, Nan Rong, Ashutosh Saxena
  8. Anytime Planning for Decentralized POMDPs using Expectation Maximization Akshat Kumar, Shlomo Zilberstein
  9. Bayesian Inference in Monte-Carlo Tree Search Gerald Tesauro, VT Rajan, Richard Segal
  10. Risk Sensitive Path Integral Control Bart van den Broek, Wim Wiegerinck, Bert Kapppen
  11. Modeling Events with Cascades of Poisson Processes Aleksandr Simma, Michael Jordan
  12. An Online Learning-based Framework for Tracking Kamalika Chaudhuri, Yoav Freund, Daniel Hsu
  13. Inference-less Density Estimation using Copula Networks Gal Elidan
  14. Maximum likelihood fitting of acyclic directed mixed graphs to binary data Robin Evans, Thomas Richardson
  15. Negative Tree Reweighted Belief Propagation Qiang Liu, Alexander Ihler
  16. Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments Bai Zhang, Yue Wang
  17. Regularized Maximum Likelihood for Intrinsic Dimension Estimation Mithun Das Gupta, Thomas Huang
  18. Intracluster Moves for Constrained Discrete-Space MCMC Firas Hamze, Nando de Freitas
  19. Sparse-posterior Gaussian Processes for general likelihoods Alan Qi, Ahmed Abdel-Gawad, Thomas Minka
  20. Formula-Based Probabilistic Inference Vibhav Gogate, Pedro Domingos
  21. Efficient clustering with limited distance information Konstantin Voevodski, Maria-Florina Balcan, Heiko Roeglin, Shang-Hua Teng, Yu Xia
  22. Learning networks determined by the ratio of prior and data Maomi Ueno
  23. Bayesian Model Averaging Using the k-best Bayesian Network Structures Jin Tian, Ru He, Lavanya Ram
  24. Gaussian Process Topic Models Amrudin Agovic, Arindam Banerjee
  25. Online Semi-Supervised Learning on Quantized Graphs Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting
  26. Bayesian exponential family projections for coupled data sources Arto Klami, Seppo Virtanen, Samuel Kaski
  27. A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation Tom Erez, William Smart
  28. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost Ping Li

Poster Session II of II: 30 papers

  1. Combining Spatial and Telemetric Features for Learning Animal Movement Models Berk Kapicioglu, Robert Schapire, Martin Wikelski, Tamara Broderick
  2. The Hierarchical Dirichlet Process Hidden Semi-Markov Model Matthew Johnson, Alan Willsky
  3. Solving Multistage Influence Diagrams using Branch-and-Bound Search Changhe Yuan, Xiaojian Wu, Eric Hansen
  4. Three new sensitivity analysis methods for influence diagrams Debarun Bhattacharjya, Ross Shachter
  5. Learning Game Representations from Data Using Rationality Constraints Alice Gao, Avi Pfeffer
  6. Rollout Sampling Policy Iteration for Decentralized POMDPs Feng Wu, Shlomo Zilberstein, Xiaoping Chen
  7. Approximating Higher-Order Distances Using Random Projections Ping Li, Michael Mahoney, Yiyuan She
  8. Possibilistic Answer Set Programming Revisited Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir
  9. Identifying Causal Effects with Computer Algebra Seth Sullivant, Luis David Garcia-Puente, Sarah Spiegelvogel
  10. Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference Raouia Ayachi, Nahla Ben Amor, Salem Benferhat , Rolf Haenni
  11. Speeding up the binary Gaussian process classification Jarno Vanhatalo, Aki Vehtari
  12. Dirichlet Process Mixtures of Generalized Mallows Models Marina Meila, Harr Chen
  13. Bayesian Rose Trees Charles Blundell, Yee Whye Teh, Katherine Heller
  14. Algorithms and Complexity Results for Exact Bayesian Structure Learning Sebastian Ordyniak, Stefan Szeider
  15. RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains Emma Brunskill, Stuart Russell
  16. A Bayesian Matrix Factorization Model for Relational Data Ajit Singh, Geoffrey Gordon
  17. Characterizing the set of coherent lower previsions with a finite number of constraints or vertices Erik Quaeghebeur
  18. Exact and Approximate Inference in Associative Hierarchical Random Fields using Graph-Cuts Chris Russell, Lubor Ladicky, Philip Torr, Pushmeet Kohli
  19. Confounding Equivalence in Causal Inference Judea Pearl, Azaria Paz
  20. Distribution over Beliefs for Memory Bounded Dec-POMDP Planning Gabriel Corona, Francois Charpillet
  21. Irregular-Time Bayesian Networks Michael Ramati, Yuval Shahar
  22. A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks Mathias Niepert
  23. Primal View on Belief Propagation Tomas Werner
  24. Inference by Minimizing Size, Divergence, or their Sum Sebastian Riedel, David Smith, Andrew McCallum
  25. Lifted Inference for Relational Continuous Models Jaesik Choi, David Hill, Eyal Amir
  26. Super-Samples from Kernel Herding Yutian Chen, Max Welling, Alex Smola
  27. Prediction with Advice of Unknown Number of Experts Alexey Chernov, Vladimir Vovk
  28. Real-Time Scheduling via Reinforcement Learning Robert Glaubius, Terry Tidwell, Christopher Gill, William Smart
  29. Variance-Based Rewards for Approximate Bayesian Reinforcement Learning Jonathan Sorg, Satinder Singh, Richard Lewis
  30. Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation Qi Guilin, Jianfeng Du, David Bell (presented by Salem Benferhat)