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26th UAI 2010: Catalina Island, CA, USA
- Peter Grünwald, Peter Spirtes:
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 J. Hsu:
An Online Learning-based Framework for Tracking. 101-108 - Yutian Chen, Max Welling, Alexander 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 P. 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 Alice 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 M. 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 J. 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 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 - Chris 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 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 Bezerra de Andrade e 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 Paul Singh, Geoffrey J. Gordon:
A Bayesian Matrix Factorization Model for Relational Data. 556-563 - Jonathan Sorg, Satinder 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 B. 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 Joseph 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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