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27th UAI 2011: Barcelona, Spain
- Fábio Gagliardi Cozman, Avi Pfeffer:
UAI 2011, Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, July 14-17, 2011. AUAI Press 2011, ISBN 978-0-9749039-7-2
Contributed Papers
- Kareem Amin, Michael J. 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 M. 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 Andrew 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 J. 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 M. Domingos:
Approximation by Quantization. 247-255 - Vibhav Gogate, Pedro M. Domingos:
Probabilistic Theorem Proving. 256-265 - Quanquan Gu, Zhenhui Li, Jiawei Han:
Generalized Fisher Score for Feature Selection. 266-273 - Andrew Guillory, Jeff A. Bilmes:
Active Semi-Supervised Learning using Submodular Functions. 274-282 - Michael Gutmann, Junichiro 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 Hoffman, Eric Brochu, Nando de Freitas:
Portfolio Allocation for Bayesian Optimization. 327-336 - Hoifung Poon, Pedro M. 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 A. 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 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 M. 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 A. 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 Ihler, Charless C. Fowlkes:
Planar Cycle Covering Graphs. 761-769 - Julian Yarkony, Ragib Morshed, Alexander Ihler, Charless C. 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. V. N. Vishwanathan:
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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