27. UAI 2011: Barcelona, Spain

Contributed Papers

• Kareem Amin, Michael Kearns, Umar Syed:
  Graphical Models for Bandit Problems. 1-10
  
• Udi Apsel, Ronen I. Brafman:
  Extended Lifted Inference with Joint Formulas. 11-18
  
• John Asmuth, Michael L. Littman:
  Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search. 19-26
  
• Yoram Bachrach, Reshef Meir, Michal Feldman, Moshe Tennenholtz:
  Solving Cooperative Reliability Games. 27-34
  
• Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani:
  Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks. 35-42
  
• Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro:
  Semi-supervised Learning with Density Based Distances. 43-50
  
• Alexander W. Blocker, Edoardo Airoldi:
  Deconvolution of mixing time series on a graph. 51-60
  
• E. Busra Celikkaya, Christian R. Shelton, William Lam:
  Factored Filtering of Continuous-Time Systems. 61-68
  
• Mithun Chakraborty, Sanmay Das, Malik Magdon-Ismail:
  Near-Optimal Target Learning With Stochastic Binary Signals. 69-76
  
• Archie C. Chapman, Simon A. Williamson, Nicholas R. Jennings:
  Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs. 77-85
  
• Laurent Charlin, Richard S. Zemel, Craig Boutilier:
  A Framework for Optimizing Paper Matching. 86-95
  
• Shaunak Chatterjee, Stuart Russell:
  A temporally abstracted Viterbi algorithm. 96-104
  
• Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing:
  Smoothing Proximal Gradient Method for General Structured Sparse Learning. 105-114
  
• Arthur Choi, Khaled S. Refaat, Adnan Darwiche:
  EDML: A Method for Learning Parameters in Bayesian Networks. 115-124
  
• SangIn Chun, Ross D. Shachter:
  Strictly Proper Mechanisms with Cooperating Players. 125-134
  
• Tom Claassen, Tom Heskes:
  A Logical Characterization of Constraint-Based Causal Discovery. 135-144
  
• Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
  Ensembles of Kernel Predictors. 145-152
  
• James Cussens:
  Bayesian network learning with cutting planes. 153-160
  
• Kun Deng, Joelle Pineau, Susan A. Murphy:
  Active Learning for Developing Personalized Treatment. 161-168
  
• Miroslav Dudík, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
  Efficient Optimal Learning for Contextual Bandits. 169-178
  
• Krishnamurthy Dvijotham, Emanuel Todorov:
  A Unifying Framework for Linearly Solvable Control. 179-186
  
• Narayanan Unny Edakunni, Gary Brown, Tim Kovacs:
  Boosting as a Product of Experts. 187-194
  
• Mahdi Milani Fard, Joelle Pineau, Csaba Szepesvári:
  PAC-Bayesian Policy Evaluation for Reinforcement Learning. 195-202
  
• Hélène Fargier, Nahla Ben Amor, Wided Guezguez:
  On the Complexity of Decision Making in Possibilistic Decision Trees. 203-210
  
• Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt:
  Inference in Probabilistic Logic Programs using Weighted CNF's. 211-220
  
• Thomas Furmston, David Barber:
  Efficient Inference in Markov Control Problems. 221-229
  
• Phan Hong Giang:
  Dynamic consistency and decision making under vacuous belief. 230-237
  
• Inmar E. Givoni, Clement Chung, Brendan J. Frey:
  Hierarchical Affinity Propagation. 238-246
  
• Vibhav Gogate, Pedro Domingos:
  Approximation by Quantization. 247-255
  
• Vibhav Gogate, Pedro Domingos:
  Probabilistic Theorem Proving. 256-265
  
• Quanquan Gu, Zhenhui Li, Jiawei Han:
  Generalized Fisher Score for Feature Selection. 266-273
  
• Andrew Guillory, Jeff Bilmes:
  Active Semi-Supervised Learning using Submodular Functions. 274-282
  
• Michael Gutmann, Jun-ichiro Hirayama:
  Bregman divergence as general framework to estimate unnormalized statistical models. 283-290
  
• Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir:
  Reasoning about RoboCup Soccer Narratives. 291-300
  
• Eric A. Hansen:
  Suboptimality Bounds for Stochastic Shortest Path Problems. 301-310
  
• Jouni Hartikainen, Simo Särkkä:
  Sequential Inference for Latent Force Models. 311-318
  
• Uri Heinemann, Amir Globerson:
  What Cannot be Learned with Bethe Approximations. 319-326
  
• Matthew D. Hoffman, Eric Brochu, Nando de Freitas:
  Portfolio Allocation for Bayesian Optimization. 327-336
  
• Hoifung Poon, Pedro Domingos:
  Sum-Product Networks: A New Deep Architecture. 337-346
  
• Jean Honorio:
  Lipschitz Parametrization of Probabilistic Graphical Models. 347-354
  
• Jonathan Huang, Ashish Kapoor, Carlos Guestrin:
  Efficient Probabilistic Inference with Partial Ranking Queries. 355-362
  
• Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
  Noisy-OR Models with Latent Confounding. 363-372
  
• Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
  Discovering causal structures in binary exclusive-or skew acyclic models. 373-382
  
• Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf:
  Detecting low-complexity unobserved causes. 383-391
  
• Nikos Karampatziakis, John Langford:
  Online Importance Weight Aware Updates. 392-399
  
• Myunghwan Kim, Jure Leskovec:
  Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model. 400-409
  
• David Knowles, Zoubin Ghahramani:
  Pitman-Yor Diffusion Trees. 410-418
  
• Alex Kulesza, Ben Taskar:
  Learning Determinantal Point Processes. 419-427
  
• Akshat Kumar, Shlomo Zilberstein:
  Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation. 428-435
  
• Minyi Li, Quoc Bao Vo, Ryszard Kowalczyk:
  An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information. 436-444
  
• Shiau Hong Lim, Peter Auer:
  Noisy Search with Comparative Feedback. 445-452
  
• Qiang Liu, Alexander T. Ihler:
  Variational Algorithms for Marginal MAP. 453-462
  
• Jérôme Louradour, Hugo Larochelle:
  Classification of Sets using Restricted Boltzmann Machines. 463-470
  
• Jianbing Ma, Weiru Liu, Paul Miller:
  Belief change with noisy sensing in the situation calculus. 471-478
  
• Brandon M. Malone, Changhe Yuan, Eric A. Hansen, Susan Bridges:
  Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search. 479-488
  
• Radu Marinescu, Nic Wilson:
  Order-of-Magnitude Influence Diagrams. 489-496
  
• Benjamin M. Marlin, Nando de Freitas:
  Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. 497-505
  
• David M. Mimno:
  Reconstructing Pompeian Households. 506-513
  
• Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
  Conditional Restricted Boltzmann Machines for Structured Output Prediction. 514-522
  
• Hala Mostafa, Victor R. Lesser:
  Compact Mathematical Programs For DEC-MDPs With Structured Agent Interactions. 523-530
  
• Ananda Narayanan B., Balaraman Ravindran:
  Fractional Moments on Bandit Problems. 531-538
  
• Swaprava Nath, Onno Zoeter, Yadati Narahari, Christopher R. Dance:
  Dynamic Mechanism Design for Markets with Strategic Resources. 539-546
  
• Nebojsa Jojic, Alessandro Perina:
  Multidimensional counting grids: Inferring word order from disordered bags of words. 547-556
  
• Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto:
  Partial Order MCMC for Structure Discovery in Bayesian Networks. 557-564
  
• Eunsoo Oh, Kee-Eung Kim:
  A Geometric Traversal Algorithm for Reward-Uncertain MDPs. 565-572
  
• Takayuki Osogami:
  Iterated risk measures for risk-sensitive Markov decision processes with discounted cost. 573-580
  
• David Pennock, Lirong Xia:
  Price Updating in Combinatorial Prediction Markets with Bayesian Networks. 581-588
  
• Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
  Identifiability of Causal Graphs using Functional Models. 589-598
  
• Barnabás Póczos, Liang Xiong, Jeff G. Schneider:
  Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. 599-608
  
• Gungor Polatkan, Oncel Tuzel:
  Compressed Inference for Probabilistic Sequential Models. 609-618
  
• Vinayak Rao, Yee Whye Teh:
  Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks. 619-626
  
• Nima Reyhani, Hideitsu Hino, Ricardo Vigário:
  New Probabilistic Bounds on Eigenvalues and Eigenvectors of Random Kernel Matrices. 627-634
  
• Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett:
  Learning with Missing Features. 635-642
  
• Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros:
  Symbolic Dynamic Programming for Discrete and Continuous State MDPs. 643-652
  
• Mark Schmidt, Karteek Alahari:
  Generalized Fast Approximate Energy Minimization via Graph Cuts: a-Expansion b-Shrink Moves. 653-660
  
• Ilya Shpitser, Thomas S. Richardson, James M. Robins:
  An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models. 661-670
  
• Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey:
  Graph Cuts is a Max-Product Algorithm. 671-680
  
• Johannes Textor, Maciej Liskiewicz:
  Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective. 681-688
  
• Inma Tur, Robert Castelo:
  Learning mixed graphical models from data with p larger than n. 689-697
  
• Maomi Ueno:
  Robust learning Bayesian networks for prior belief. 698-707
  
• Joop van de Ven, Fabio Ramos:
  Distributed Anytime MAP Inference. 708-716
  
• Greg Ver Steeg, Aram Galstyan:
  A Sequence of Relaxation Constraining Hidden Variable Models. 717-726
  
• Michael P. Wellman, Lu Hong, Scott E. Page:
  The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information. 727-735
  
• Andrew Gordon Wilson, Zoubin Ghahramani:
  Generalised Wishart Processes. 736-744
  
• Feng Yan, Zenglin Xu, Yuan (Alan) Qi:
  Sparse matrix-variate Gaussian process blockmodels for network modeling. 745-752
  
• Jian-Bo Yang, Ivor W. Tsang:
  Hierarchical Maximum Margin Learning for Multi-Class Classification. 753-760
  
• Julian Yarkony, Alexander T. Ihler, Charless Fowlkes:
  Planar Cycle Covering Graphs. 761-769
  
• Julian Yarkony, Ragib Morshed, Alexander T. Ihler, Charless Fowlkes:
  Tightening MRF Relaxations with Planar Subproblems. 770-777
  
• Yaoliang Yu, Dale Schuurmans:
  Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. 778-785
  
• Haohai Yu, Robert van Engelen:
  Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test. 786-795
  
• Chao Zhang, Dacheng Tao:
  Risk Bounds for Infinitely Divisible Distribution. 796-803
  
• Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
  Kernel-based Conditional Independence Test and Application in Causal Discovery. 804-813
  
• Xinhua Zhang, Ankan Saha, S. Vishwanatan:
  Smoothing Multivariate Performance Measures. 814-821
  
• Lu Zheng, Ole J. Mengshoel, Jike Chong:
  Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization. 822-830
  
• Jun Zhu, Eric P. Xing:
  Sparse Topical Coding. 831-838
  
• Jakob Zscheischler, Dominik Janzing, Kun Zhang:
  Testing whether linear equations are causal: A free probability theory approach. 839-846
  
• Ruggiero Cavallo:
  Incentives in Group Decision-Making With Uncertainty and Subjective Beliefs. 849
  

Abstracts

• Diego Colombo, Marloes H. Maathuis, Markus Kalisch, Thomas S. Richardson:
  Learning high-dimensional DAGs with latent and selection variables (Abstract). 850
  
• Alain Hauser, Peter Bühlmann:
  Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract). 851
  
• Jennifer Listgarten, Carl Myers Kadie, Eric E. Schadt, David Heckerman:
  Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract). 852
  
• Greg Ver Steeg, Aram Galstyan, Armen E. Allahverdyan:
  Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs (Abstract). 853
  

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