26. UAI 2010:
Catalina Island,
CA,
USA
Peter Grünwald, Peter Spirtes (Eds.):
UAI 2010, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, July 8-11, 2010.
AUAI Press 2010, ISBN 978-0-9749039-6-5
- Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes.
1-9
- Amrudin Agovic, Arindam Banerjee:
Gaussian Process Topic Models.
10-19
- Amr Ahmed, Eric P. Xing:
Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream.
20-29
- Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell:
Gibbs Sampling in Open-Universe Stochastic Languages.
30-39
- Raouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni:
Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference.
40-47
- Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir:
Possibilistic Answer Set Programming Revisited.
48-55
- Debarun Bhattacharjya, Ross D. Shachter:
Three new sensitivity analysis methods for influence diagrams.
56-64
- Charles Blundell, Yee Whye Teh, Katherine A. Heller:
Bayesian Rose Trees.
65-72
- Matthias Bröcheler, Lilyana Mihalkova, Lise Getoor:
Probabilistic Similarity Logic.
73-82
- Emma Brunskill, Stuart Russell:
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains.
83-92
- Alan Carlin, Nathan Schurr, Janusz Marecki:
ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards.
93-100
- Kamalika Chaudhuri, Yoav Freund, Daniel Hsu:
An Online Learning-based Framework for Tracking.
101-108
- Yutian Chen, Max Welling, Alex J. Smola:
Super-Samples from Kernel Herding.
109-116
- Alexey V. Chernov, Vladimir Vovk:
Prediction with Advice of Unknown Number of Experts.
117-125
- Jaesik Choi, Eyal Amir, David J. Hill:
Lifted Inference for Relational Continuous Models.
126-134
- Gabriel Corona, François Charpillet:
Distribution over Beliefs for Memory Bounded Dec-POMDP Planning.
135-142
- Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations.
143-150
- Gal Elidan:
Inference-less Density Estimation using Copula Bayesian Networks.
151-159
- Tom Erez, William D. Smart:
A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation.
160-167
- Stefano Ermon, Jon Conrad, Carla Gomes, Bart Selman:
Playing games against nature: optimal policies for renewable resource allocation.
168-176
- Robin J. Evans, Thomas S. Richardson:
Maximum likelihood fitting of acyclic directed mixed graphs to binary data.
177-184
- Xi Gao, Avi Pfeffer:
Learning Game Representations from Data Using Rationality Constraints.
185-192
- Luis Garcia, Sarah Spielvogel, Seth Sullivant:
Identifying Causal Effects with Computer Algebra.
193-200
- Robert Glaubius, Terry Tidwell, Christopher D. Gill, William D. Smart:
Real-Time Scheduling via Reinforcement Learning.
201-209
- Vibhav Gogate, Pedro Domingos:
Formula-Based Probabilistic Inference.
210-219
- Mithun Das Gupta, Thomas S. Huang:
Regularized Maximum Likelihood for Intrinsic Dimension Estimation.
220-227
- Joseph Y. Halpern, Nan Rong, Ashutosh Saxena:
MDPs with Unawareness.
228-235
- Firas Hamze, Nando de Freitas:
Intracluster Moves for Constrained Discrete-Space MCMC.
236-243
- Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu:
Robust Metric Learning by Smooth Optimization.
244-251
- Matthew Johnson, Alan S. Willsky:
The Hierarchical Dirichlet Process Hidden Semi-Markov Model.
252-259
- Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick:
Combining Spatial and Telemetric Features for Learning Animal Movement Models.
260-267
- Kalev Kask, Rina Dechter, Andrew Gelfand:
BEEM : Bucket Elimination with External Memory.
268-276
- Kevin T. Kelly, Conor Mayo-Wilson:
Causal Conclusions that Flip Repeatedly and Their Justification.
277-285
- Arto Klami, Seppo Virtanen, Samuel Kaski:
Bayesian exponential family projections for coupled data sources.
286-293
- Akshat Kumar, Shlomo Zilberstein:
Anytime Planning for Decentralized POMDPs using Expectation Maximization.
294-301
- Ping Li:
Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost.
302-311
- Ping Li, Michael W. Mahoney, Yiyuan She:
Approximating Higher-Order Distances Using Random Projections.
312-321
- Yijing Li, Prakash P. Shenoy:
Solving Hybrid Influence Diagrams with Deterministic Variables.
322-331
- Qiang Liu, Alexander T. Ihler:
Negative Tree Reweighted Belief Propagation.
332-339
- Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein:
GraphLab: A New Framework For Parallel Machine Learning.
340-349
- Qi Mao, Ivor W. Tsang:
Parameter-Free Spectral Kernel Learning.
350-357
- Marina Meila, Harr Chen:
Dirichlet Process Mixtures of Generalized Mallows Models.
358-367
- Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Parametric Return Density Estimation for Reinforcement Learning.
368-375
- Enrique Munoz de Cote, Archie C. Chapman, Adam M. Sykulski, Nicholas R. Jennings:
Automated Planning in Repeated Adversarial Games.
376-383
- Mathias Niepert:
A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks.
384-391
- Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Yungjen Tung, Allen Caine:
Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker.
392-400
- Sebastian Ordyniak, Stefan Szeider:
Algorithms and Complexity Results for Exact Bayesian Structure Learning.
401-408
- Thorsten J. Ottosen, Finn Verner Jensen:
The Cost of Troubleshooting Cost Clusters with Inside Information.
409-416
- Judea Pearl:
On a Class of Bias-Amplifying Variables that Endanger Effect Estimates.
417-424
- Judea Pearl:
On Measurement Bias in Causal Inference.
425-432
- Judea Pearl, Azaria Paz:
Confounding Equivalence in Causal Inference.
433-441
- Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen:
A Family of Computationally E cient and Simple Estimators for Unnormalized Statistical Models.
442-449
- Yuan (Alan) Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka:
Sparse-posterior Gaussian Processes for general likelihoods.
450-457
- Guilin Qi, Jianfeng Du, Weiru Liu, David A. Bell:
Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation.
458-465
- Erik Quaeghebeur:
Characterizing the Set of Coherent Lower Previsions with a Finite Number of Constraints or Vertices.
466-473
- Raghuram Ramanujan, Ashish Sabharwal, Bart Selman:
Understanding Sampling Style Adversarial Search Methods.
474-483
- Michael Ramati, Yuval Shahar:
Irregular-Time Bayesian Networks.
484-491
- Sebastian Riedel, David A. Smith, Andrew McCallum:
Inference by Minimizing Size, Divergence, or their Sum.
492-499
- Nicholas Ruozzi, Sekhar Tatikonda:
Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models.
500
- Christopher Russell, Lubor Ladicky, Pushmeet Kohli, Philip H. S. Torr:
Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts.
501-508
- Ross D. Shachter, Debarun Bhattacharjya:
Dynamic programming in in uence diagrams with decision circuits.
509-516
- Daniel Sheldon, Bistra N. Dilkina, Adam N. Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla P. Gomes, David B. Shmoys, William Allen, Ole Amundsen, William Vaughan:
Maximizing the Spread of Cascades Using Network Design.
517-526
- Ilya Shpitser, Tyler J. VanderWeele, James M. Robins:
On the Validity of Covariate Adjustment for Estimating Causal Effects.
527-536
- Ricardo Silva, Robert B. Gramacy:
Gaussian Process Structural Equation Models with Latent Variables.
537-545
- Aleksandr Simma, Michael I. Jordan:
Modeling Events with Cascades of Poisson Processes.
546-555
- Ajit Singh, Geoffrey J. Gordon:
A Bayesian Matrix Factorization Model for Relational Data.
556-563
- Jonathan Sorg, Satinder P. Singh, Richard L. Lewis:
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning.
564-571
- Ameet Talwalkar, Afshin Rostamizadeh:
Matrix Coherence and the Nystrom Method.
572-579
- Gerald Tesauro, V. T. Rajan, Richard Segal:
Bayesian Inference in Monte-Carlo Tree Search.
580-588
- Jin Tian, Ru He, Lavanya Ram:
Bayesian Model Averaging Using the k-best Bayesian Network Structures.
589-597
- Maomi Ueno:
Learning networks determined by the ratio of prior and data.
598-605
- Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting:
Online Semi-Supervised Learning on Quantized Graphs.
606-614
- Bart van den Broek, Wim Wiegerinck, Hilbert J. Kappen:
Risk Sensitive Path Integral Control.
615-622
- Jarno Vanhatalo, Aki Vehtari:
Speeding up the binary Gaussian process classification.
623-631
- Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia:
Efficient Clustering with Limited Distance Information.
632-640
- Mark Voortman, Denver Dash, Marek J. Druzdzel:
Learning Why Things Change: The Difference-Based Causality Learner.
641-650
- Tomás Werner:
Primal View on Belief Propagation.
651-657
- Jens Witkowski:
Truthful Feedback for Sanctioning Reputation Mechanisms.
658-665
- Feng Wu, Shlomo Zilberstein, Xiaoping Chen:
Rollout Sampling Policy Iteration for Decentralized POMDPs.
666-673
- Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy:
Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario.
674-682
- Shuang-Hong Yang, Jiang Bian, Hongyuan Zha:
Hybrid Generative/Discriminative Learning for Automatic Image Annotation.
683-690
- Changhe Yuan, XiaoJian Wu, Eric A. Hansen:
Solving Multistage Influence Diagrams using Branch-and-Bound Search.
691-700
- Bai Zhang, Yue Wang:
Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments.
701-708
- Kun Zhang, Aapo Hyvärinen:
Source Separation and Higher-Order Causal Analysis of MEG and EEG.
709-716
- Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery.
717-724
- Yu Zhang, Bin Cao, Dit-Yan Yeung:
Multi-Domain Collaborative Filtering.
725-732
- Yu Zhang, Dit-Yan Yeung:
A Convex Formulation for Learning Task Relationships in Multi-Task Learning.
733-442
- Qian Zhu, Branislav Kveton, Lily B. Mummert, Padmanabhan Pillai:
Automatic Tuning of Interactive Perception Applications.
743-751
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