Overview of the ICML, UAI, COLT and MLG 2008 Programme

UAI 2008 Schedule

All pleanary sessions will take place in lecture room PI of the Porthania building at Yliopistonkatu 3. The program leaflet contains more information about the venue and practical details.

The plenary talks are 20 minutes long each. After each talk there is a 5 minute break for questions and for changing the speaker. (N.B. The session titles are only indicative and do not necessarily reflect accurately the topic of all the papers in the session.)

Thursday, July 10

8:45-9:00 Opening Remarks

9:00-10:15 Topic Models and Clustering (Chair: Mark Johnson)
- David Mimno, Andrew McCallum: Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
- Amit Gruber, Michal Rosen Zvi, Yair Weiss: Latent Topic Models for Hypertext
- Daniel Tarlow, Richard Zemel, Brendan Frey: Flexible Priors for Exemplar-based Clustering

10:15-10:40 Coffee Break

10:40-11:40 Invited Talk
- Peter Grünwald: The Catch-Up Phenomenon in Bayesian Inference

11:45-13:00 Inference I (Chair: Ben Taskar)
- Tamir Hazan, Amnon Shashua: Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies (Best student paper runner up)
- David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing (Best paper award)
- Justin Domke: Learning Convex Inference of Marginals

13:00-14:30 Lunch Break (AUAI chairs meeting)

14:30-15:30 Invited Talk
- Robin Hanson: Combinatorial Prediction Markets

15:35-16:50 Games and Decisions (Chair: Stuart Russell)
- Sevan Ficici, David Parkes, Avi Pfeffer: Learning and Solving Many-Player Games through a Cluster-Based Representation
- Enrique Munoz de Cote, Michael Littman: A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games (Best student paper award)
- Cassio de Campos, Qiang Ji: Strategy Selection in Influence Diagrams using Imprecise Probabilities

16:50-17:15 Coffee Break

17:15-18:15 Poster spotlights - 40 spotlight presentations, 1.5 minutes each, one slide/spotlight

18:15-21:00 Poster session

Friday, July 11

9:00-10:15 Multi-View and Transfer Learning (Chair: Zoubin Ghahramani)
- Kuzman Ganchev, Joao Graca, John Blitzer, Ben Taskar: Multi-View Learning over Structured and Non-Identical Outputs
- C. Mario Christoudias, Raquel Urtasun, Trevor Darrell: Multi-View Learning in the Presence of View Disagreement
- Gal Elidan, Benjamin Packer, Geremy Heitz, Daphne Koller: Convex Point Estimation using Undirected Bayesian Transfer Hierarchies

10:15-10:40 Coffee Break

10:40-11:40 Invited Talk
- Gabor Lugosi: Concentration inequalities

11:45-13:00 Reinforcement learning (Chair: Rich Sutton)
- Emma Brunskill, Bethany Leffler, Lihong Li, Michael Littman, Nicholas Roy: CORL: A Continuous-state Offset-dynamics Reinforcement Learner
- Branislav Kveton, Milos Hauskrecht: Partitioned Linear Programming Approximations for MDPs
- Marc Toussaint, Laurent Charlin, Pascal Poupart: Hierarchical POMDP Controller Optimization by Likelihood Maximization (Best paper runner up)

13:00-14:30 Lunch Break

14:30-15:30 Invited Talk
- Dan Klein: Unsupervised Learning for Natural Language Processing

15:35-16:50 Causality (Chair: Andrew Ng)
- Ulf Nielsen, Jean-Philippe Pellet, André Elisseeff: Explanation Trees for Causal Bayesian Networks
- A. Philip Dawid, Vanessa Didelez: Identifying Optimal Sequential Decisions
- Jin Tian: Identifying Dynamic Sequential Plans

16:50-17:15 Coffee Break

17:15-18:30 Network learning (Chair: Pascal Poupart)
- Harald Steck: Learning the Bayesian Network Structure: Dirichlet Prior vs. Data
- Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik Hoyer: Discovering Cyclic Causal Models by Independent Components Analysis
- Varun Ganapathi, David Vickrey, John Duchi, Daphne Koller: Constrained Approximate Maximum Entropy Learning of Markov Random Fields

18:30-19:55 UAI Business Meeting

20:00-23:00 UAI/COLT Joint Banquet

Saturday, July 12

9:00-10:15 Modeling and Regression (Chair: Amir Globerson)
- Keith Noto, Mark Craven: Learning Hidden Markov Models for Regression using Path Aggregation
- Seyoung Kim, Eric Xing: Feature Selection via Block-Regularized Regression
- David Barber: Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices

10:15-10:40 Coffee Break

10:40-12:20 Inference II (Chair: Tommi Jaakkola)
- Arthur Choi, Adnan Darwiche: Approximating the Partition Function by Deleting and then Correcting for Model Edges
- Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha: Complexity of Inference in Graphical Models
- Peter Thwaites, Jim Smith, Robert Cowell: Propagation using Chain Event Graphs
- Tal El-Hay, Nir Friedman, Raz Kupferman: Gibbs Sampling in Factorized Continuous-Time Markov Processes (Best student paper runner up)

12:20-13:50 Lunch Break

13:50-15:30 Modeling (Chair: Hal Daume)
- Noah Goodman, Vikash Mansinghka, Daniel Roy, Keith Bonawitz, Joshua Tenenbaum: Church: a language for generative models
- Mathias Niepert, Dirk Van Gucht, Marc Gyssens: On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach (Best student paper runner up)
- Hannaneh Hajishirzi, Eyal Amir: Sampling First Order Logical Particles
- Jim Huang, Brendan Frey: Cumulative distribution networks and the derivative-sum-product algorithm (Best student paper runner up)

15:30 End of the conference

Accepted UAI posters

The poster session takes place on Thursday, July 10, at 5:15pm-9pm (starting with a one hour poster spotlight session).

01	Umut A. Acar, Alexander Ihler, Ramgopal R. Mettu, Özgür Sumer	Adaptive inference on general graphical models
02	Dimitrios Antos, Avi Pfeffer	Identifying reasoning patterns in games
03	Vincent Auvray, Louis Wehenkel	Learning Inclusion-Optimal Chordal Graphs
04	Debarun Bhattacharjya, Ross Shachter	Sensitivity analysis in decision circuits
05	Liefeng Bo, Cristian Sminchisescu	Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression
06	Zhihong Cai, Manabu Kuroki	On Identifying Total Effects in the Presence of Latent Variables and Selection bias
07	Botond Cseke, Tom Heskes	Bounds on the Bethe Free Energy for Gaussian Networks
08	James Cussens	Bayesian network learning by compiling to weighted MAX-SAT
09	Gert De Cooman, Filip Hermans, Erik Quaeghebeur	Sensitivity analysis for finite Markov chains in discrete time
10	John Duchi, Stephen Gould, Daphne Koller	Projected Subgradient Methods for Learning Sparse Gaussians
11	Quang Duong, Michael Wellman, Satinder Singh	Knowledge Combination in Graphical Multiagent Models
12	Frederick Eberhardt	Almost Optimal Intervention Sets for Causal Discovery
13	Vibhav Gogate, Rina Dechter	AND/OR Importance Sampling
14	Peter Grunwald, Joe Halpern	A Game-Theoretic Analysis of Updating Sets of Probabilities
15	Eric Hansen	Sparse Stochastic Finite-State Controllers for POMDPs
16	Greg Hines, Kate Larson	Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium
17	Patrik Hoyer, Aapo Hyvarinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda, Shohei Shimizu	Causal discovery of linear acyclic models with arbitrary distributions
18	Bowen Hui, Craig Boutilier	Toward Experiential Utility Elicitation for Interface Customization
19	Alejandro Isaza, Csaba Szepesvari, Vadim Bulitko, Russell Greiner	Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction
20	Tony Jebara	Bayesian Out-Trees
21	Manabu Kuroki, Zhihong Cai	The Evaluation of Causal Effects in Studies with an Unobserved Exposure/Outcome Variable: Bounds and Identification
22	Johan Kwisthout, Linda van der Gaag	The Computational Complexity of Sensitivity Analysis and Parameter Tuning
23	Eric Laber, Susan Murphy	Small Sample Inference for Generalization Error in Classification Using the CUD Bound
24	Gregory Lawrence, Stuart Russell	Improving Gradient Estimation by Incorporating Sensor Data
25	Daniel Lowd, Pedro Domingos	Learning Arithmetic Circuits
26	Marina Meila, Le Bao	Estimation and clustering with infinite rankings
27	Kurt Miller, Thomas Griffiths, Michael I. Jordan	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
28	Lars Otten, Rina Dechter	Bounding Search Space Size via (Hyper)tree Decompositions
29	Yan Radovilsky, Eyal Shimony	Observation Subset Selection as Local Compilation of Performance Profiles
30	Sebastian Riedel	Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
31	Stephane Ross, Joelle Pineau	Model-Based Bayesian Reinforcement Learning in Large Structured Domains
32	Aleksandr Simma, Moises Goldszmidt, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs, Richard Mortier	CT-NOR: Representing and Reasoning About Events in Continuous Time
33	Tomas Singliar, Denver Dash	Efficient Inference in Persistent Dynamic Bayesian Networks
34	Matthew Streeter, Stephen Smith	New Techniques for Algorithm Portfolio Design
35	Richard Sutton, Csaba Szepesvari, Alborz Geramifard, Michael Bowling	Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
36	Jarno Vanhatalo, Aki Vehtari	Modelling local and global phenomena with sparse Gaussian processes
37	Chong Wang, David Blei, David Heckerman	Continuous Time Dynamic Topic Models
38	Max Welling, Yee Whye Teh, Bert Kappen	Hybrid Variational/Gibbs Collapsed Inference in Topic Models
39	Ydo Wexler, Christopher Meek	Inference for Multiplicative Models
40	Haohai Yu, Robert van Engelen	Refractor Importance Sampling