UAI 2011

101 papers

A Framework for Optimizing Paper Matching Laurent Charlin, Richard S. Zemel, Craig Boutilier

A Geometric Traversal Algorithm for Reward-Uncertain MDPs Eunsoo Oh, Kee-Eung Kim

A Logical Characterization of Constraint-Based Causal Discovery Tom Claassen, Tom Heskes

A Sequence of Relaxation Constraining Hidden Variable Models Greg Ver Steeg, Aram Galstyan

A Temporally Abstracted Viterbi Algorithm Shaunak Chatterjee, Stuart Russell

A Unifying Framework for Linearly Solvable Control Krishnamurthy Dvijotham, Emanuel Todorov

Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani

Active Learning for Developing Personalized Treatment Kun Deng, Joelle Pineau, Susan A. Murphy

Active Semi-Supervised Learning Using Submodular Functions Andrew Guillory, Jeff A. Bilmes

Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective Johannes Textor, Maciej Liskiewicz

An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models Ilya Shpitser, Thomas S. Richardson, James M. Robins

An Efficient Protocol for Negotiation over Combinatorial Domains with Incomplete Information Minyi Li, Quoc Bao Vo, Ryszard Kowalczyk

Approximation by Quantization Vibhav Gogate, Pedro M. Domingos

Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood Benjamin M. Marlin, Nando de Freitas

Bayesian Network Learning with Cutting Planes James Cussens

Belief Change with Noisy Sensing in the Situation Calculus Jianbing Ma, Weiru Liu, Paul Miller

Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization Lu Zheng, Ole J. Mengshoel, Jike Chong

Boosting as a Product of Experts Narayanan Unny Edakunni, Gary Brown, Tim Kovacs

Bregman Divergence as General Framework to Estimate Unnormalized Statistical Models Michael Gutmann, Junichiro Hirayama

Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract) Alain Hauser, Peter Bühlmann

Classification of Sets Using Restricted Boltzmann Machines Jérôme Louradour, Hugo Larochelle

Compact Mathematical Programs for DEC-MDPs with Structured Agent Interactions Hala Mostafa, Victor R. Lesser

Compressed Inference for Probabilistic Sequential Models Gungor Polatkan, Oncel Tuzel

Conditional Restricted Boltzmann Machines for Structured Output Prediction Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton

Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract) Jennifer Listgarten, Carl Myers Kadie, Eric E. Schadt, David Heckerman

Deconvolution of Mixing Time Series on a Graph Alexander W. Blocker, Edoardo M. Airoldi

Detecting Low-Complexity Unobserved Causes Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf

Discovering Causal Structures in Binary Exclusive-or Skew Acyclic Models Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

Distributed Anytime MAP Inference Joop van de Ven, Fabio Ramos

Dynamic Consistency and Decision Making Under Vacuous Belief Phan Hong Giang

Dynamic Mechanism Design for Markets with Strategic Resources Swaprava Nath, Onno Zoeter, Yadati Narahari, Christopher R. Dance

EDML: A Method for Learning Parameters in Bayesian Networks Arthur Choi, Khaled S. Refaat, Adnan Darwiche

Efficient Inference in Markov Control Problems Thomas Furmston, David Barber

Efficient Optimal Learning for Contextual Bandits Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang

Efficient Probabilistic Inference with Partial Ranking Queries Jonathan Huang, Ashish Kapoor, Carlos Guestrin

Ensembles of Kernel Predictors Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Extended Lifted Inference with Joint Formulas Udi Apsel, Ronen I. Brafman

Factored Filtering of Continuous-Time Systems E. Busra Celikkaya, Christian R. Shelton, William Lam

Fast MCMC Sampling for Markov Jump Processes and Continuous Time Bayesian Networks Vinayak A. Rao, Yee Whye Teh

Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs Archie C. Chapman, Simon Andrew Williamson, Nicholas R. Jennings

Fractional Moments on Bandit Problems Ananda Narayanan B., Balaraman Ravindran

Generalised Wishart Processes Andrew Gordon Wilson, Zoubin Ghahramani

Generalized Fast Approximate Energy Minimization via Graph Cuts: A-Expansion B-Shrink Moves Mark Schmidt, Karteek Alahari

Generalized Fisher Score for Feature Selection Quanquan Gu, Zhenhui Li, Jiawei Han

Graph Cuts Is a Max-Product Algorithm Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey

Graphical Models for Bandit Problems Kareem Amin, Michael J. Kearns, Umar Syed

Hierarchical Affinity Propagation Inmar E. Givoni, Clement Chung, Brendan J. Frey

Hierarchical Maximum Margin Learning for Multi-Class Classification Jian-Bo Yang, Ivor W. Tsang

Identifiability of Causal Graphs Using Functional Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search Brandon M. Malone, Changhe Yuan, Eric A. Hansen, Susan Bridges

Incentives in Group Decision-Making with Uncertainty and Subjective Beliefs Ruggiero Cavallo

Inference in Probabilistic Logic Programs Using Weighted CNF's Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt

Iterated Risk Measures for Risk-Sensitive Markov Decision Processes with Discounted Cost Takayuki Osogami

Kernel-Based Conditional Independence Test and Application in Causal Discovery Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Learning Determinantal Point Processes Alex Kulesza, Ben Taskar

Learning High-Dimensional DAGs with Latent and Selection Variables (Abstract) Diego Colombo, Marloes H. Maathuis, Markus Kalisch, Thomas S. Richardson

Learning Is Planning: Near Bayes-Optimal Reinforcement Learning via Monte-Carlo Tree Search John Asmuth, Michael L. Littman

Learning Mixed Graphical Models from Data with P Larger than N Inma Tur, Robert Castelo

Learning with Missing Features Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett

Lipschitz Parametrization of Probabilistic Graphical Models Jean Honorio

Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: The K-Test Haohai Yu, Robert van Engelen

Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation Akshat Kumar, Shlomo Zilberstein

Modeling Social Networks with Node Attributes Using the Multiplicative Attribute Graph Model Myunghwan Kim, Jure Leskovec

Multidimensional Counting Grids: Inferring Word Order from Disordered Bags of Words Nebojsa Jojic, Alessandro Perina

Near-Optimal Target Learning with Stochastic Binary Signals Mithun Chakraborty, Sanmay Das, Malik Magdon-Ismail

New Probabilistic Bounds on Eigenvalues and Eigenvectors of Random Kernel Matrices Nima Reyhani, Hideitsu Hino, Ricardo Vigário

Noisy Search with Comparative Feedback Shiau Hong Lim, Peter Auer

Noisy-or Models with Latent Confounding Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions Barnabás Póczos, Liang Xiong, Jeff G. Schneider

On the Complexity of Decision Making in Possibilistic Decision Trees Hélène Fargier, Nahla Ben Amor, Wided Guezguez

Online Importance Weight Aware Updates Nikos Karampatziakis, John Langford

Order-of-Magnitude Influence Diagrams Radu Marinescu, Nic Wilson

PAC-Bayesian Policy Evaluation for Reinforcement Learning Mahdi Milani Fard, Joelle Pineau, Csaba Szepesvári

Partial Order MCMC for Structure Discovery in Bayesian Networks Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto

Pitman-Yor Diffusion Trees David A. Knowles, Zoubin Ghahramani

Planar Cycle Covering Graphs Julian Yarkony, Alexander Ihler, Charless C. Fowlkes

Portfolio Allocation for Bayesian Optimization Matthew Hoffman, Eric Brochu, Nando de Freitas

Price Updating in Combinatorial Prediction Markets with Bayesian Networks David M. Pennock, Lirong Xia

Probabilistic Theorem Proving Vibhav Gogate, Pedro M. Domingos

Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering Yaoliang Yu, Dale Schuurmans

Reasoning About RoboCup Soccer Narratives Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir

Reconstructing Pompeian Households David M. Mimno

Risk Bounds for Infinitely Divisible Distribution Chao Zhang, Dacheng Tao

Robust Learning Bayesian Networks for Prior Belief Maomi Ueno

Semi-Supervised Learning with Density Based Distances Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro

Sequential Inference for Latent Force Models Jouni Hartikainen, Simo Särkkä

Smoothing Multivariate Performance Measures Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan

Smoothing Proximal Gradient Method for General Structured Sparse Learning Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing

Solving Cooperative Reliability Games Yoram Bachrach, Reshef Meir, Michal Feldman, Moshe Tennenholtz

Sparse Matrix-Variate Gaussian Process Blockmodels for Network Modeling Feng Yan, Zenglin Xu, Yuan (Alan) Qi

Sparse Topical Coding Jun Zhu, Eric P. Xing

Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs (Abstract) Greg Ver Steeg, Aram Galstyan, Armen E. Allahverdyan

Strictly Proper Mechanisms with Cooperating Players SangIn Chun, Ross D. Shachter

Suboptimality Bounds for Stochastic Shortest Path Problems Eric A. Hansen

Sum-Product Networks: A New Deep Architecture Hoifung Poon, Pedro M. Domingos

Symbolic Dynamic Programming for Discrete and Continuous State MDPs Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros

Testing Whether Linear Equations Are Causal: A Free Probability Theory Approach Jakob Zscheischler, Dominik Janzing, Kun Zhang

The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information Michael P. Wellman, Lu Hong, Scott E. Page

Tightening MRF Relaxations with Planar Subproblems Julian Yarkony, Ragib Morshed, Alexander Ihler, Charless C. Fowlkes

Variational Algorithms for Marginal MAP Qiang Liu, Alexander Ihler

What Cannot Be Learned with Bethe Approximations Uri Heinemann, Amir Globerson