26. UAI 2010: Catalina Island, CA, USA

• 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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