ML Anthology

UAI 2010

88 papers

A Bayesian Matrix Factorization Model for Relational Data Ajit Paul Singh, Geoffrey J. Gordon
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A Convex Formulation for Learning Task Relationships in Multi-Task Learning Yu Zhang, Dit-Yan Yeung
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A Delayed Column Generation Strategy for Exact K-Bounded MAP Inference in Markov Logic Networks Mathias Niepert
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A Family of Computationally E Cient and Simple Estimators for Unnormalized Statistical Models Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen
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A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation Tom Erez, William D. Smart
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ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft Hazards Alan Carlin, Nathan Schurr, Janusz Marecki
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Algorithms and Complexity Results for Exact Bayesian Structure Learning Sebastian Ordyniak, Stefan Szeider
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An Online Learning-Based Framework for Tracking Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
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Anytime Planning for Decentralized POMDPs Using Expectation Maximization Akshat Kumar, Shlomo Zilberstein
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Approximating Higher-Order Distances Using Random Projections Ping Li, Michael W. Mahoney, Yiyuan She
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Automated Planning in Repeated Adversarial Games Enrique Munoz de Cote, Archie C. Chapman, Adam M. Sykulski, Nicholas R. Jennings
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Automatic Tuning of Interactive Perception Applications Qian Zhu, Branislav Kveton, Lily B. Mummert, Padmanabhan Pillai
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Bayesian Exponential Family Projections for Coupled Data Sources Arto Klami, Seppo Virtanen, Samuel Kaski
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Bayesian Inference in Monte-Carlo Tree Search Gerald Tesauro, V. T. Rajan, Richard B. Segal
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Bayesian Model Averaging Using the K-Best Bayesian Network Structures Jin Tian, Ru He, Lavanya Ram
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Bayesian Rose Trees Charles Blundell, Yee Whye Teh, Katherine A. Heller
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BEEM : Bucket Elimination with External Memory Kalev Kask, Rina Dechter, Andrew Gelfand
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Causal Conclusions That Flip Repeatedly and Their Justification Kevin T. Kelly, Conor Mayo-Wilson
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Characterizing the Set of Coherent Lower Previsions with a Finite Number of Constraints or Vertices Erik Quaeghebeur
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Combining Spatial and Telemetric Features for Learning Animal Movement Models Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick
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Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Yungjen Tung, Allen Caine
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Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference Raouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni
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Confounding Equivalence in Causal Inference Judea Pearl, Azaria Paz
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Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical Models Nicholas Ruozzi, Sekhar Tatikonda
Dirichlet Process Mixtures of Generalized Mallows Models Marina Meila, Harr Chen
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Distribution over Beliefs for Memory Bounded Dec-POMDP Planning Gabriel Corona, François Charpillet
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Dynamic Programming in in Uence Diagrams with Decision Circuits Ross D. Shachter, Debarun Bhattacharjya
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Efficient Clustering with Limited Distance Information Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia
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Exact and Approximate Inference in Associative Hierarchical Networks Using Graph Cuts Chris Russell, Lubor Ladicky, Pushmeet Kohli, Philip H. S. Torr
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Formula-Based Probabilistic Inference Vibhav Gogate, Pedro M. Domingos
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Gaussian Process Structural Equation Models with Latent Variables Ricardo Bezerra de Andrade e Silva, Robert B. Gramacy
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Gaussian Process Topic Models Amrudin Agovic, Arindam Banerjee
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Gibbs Sampling in Open-Universe Stochastic Languages Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell
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GraphLab: A New Framework for Parallel Machine Learning Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein
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Hybrid Generative/Discriminative Learning for Automatic Image Annotation Shuang-Hong Yang, Jiang Bian, Hongyuan Zha
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Identifying Causal Effects with Computer Algebra Luis Garcia, Sarah Spielvogel, Seth Sullivant
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Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes Ryan Prescott Adams, George E. Dahl, Iain Murray
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Inference by Minimizing Size, Divergence, or Their Sum Sebastian Riedel, David A. Smith, Andrew McCallum
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Inference-Less Density Estimation Using Copula Bayesian Networks Gal Elidan
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Inferring Deterministic Causal Relations Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf
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Intracluster Moves for Constrained Discrete-Space MCMC Firas Hamze, Nando de Freitas
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Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery Kun Zhang, Bernhard Schölkopf, Dominik Janzing
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Irregular-Time Bayesian Networks Michael Ramati, Yuval Shahar
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Learning Game Representations from Data Using Rationality Constraints Xi Alice Gao, Avi Pfeffer
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Learning Networks Determined by the Ratio of Prior and Data Maomi Ueno
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Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments Bai Zhang, Yue Joseph Wang
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Learning Why Things Change: The Difference-Based Causality Learner Mark Voortman, Denver Dash, Marek J. Druzdzel
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Lifted Inference for Relational Continuous Models Jaesik Choi, Eyal Amir, David J. Hill
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Matrix Coherence and the Nystrom Method Ameet Talwalkar, Afshin Rostamizadeh
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Maximizing the Spread of Cascades Using Network Design 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
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Maximum Likelihood Fitting of Acyclic Directed Mixed Graphs to Binary Data Robin J. Evans, Thomas S. Richardson
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MDPs with Unawareness Joseph Y. Halpern, Nan Rong, Ashutosh Saxena
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Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation Guilin Qi, Jianfeng Du, Weiru Liu, David A. Bell
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Modeling Events with Cascades of Poisson Processes Aleksandr Simma, Michael I. Jordan
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Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy
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Multi-Domain Collaborative Filtering Yu Zhang, Bin Cao, Dit-Yan Yeung
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Negative Tree Reweighted Belief Propagation Qiang Liu, Alexander Ihler
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On a Class of Bias-Amplifying Variables That Endanger Effect Estimates Judea Pearl
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On Measurement Bias in Causal Inference Judea Pearl
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On the Validity of Covariate Adjustment for Estimating Causal Effects Ilya Shpitser, Tyler J. VanderWeele, James M. Robins
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Online Semi-Supervised Learning on Quantized Graphs Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting
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Parameter-Free Spectral Kernel Learning Qi Mao, Ivor W. Tsang
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Parametric Return Density Estimation for Reinforcement Learning Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka
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Playing Games Against Nature: Optimal Policies for Renewable Resource Allocation Stefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman
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Possibilistic Answer Set Programming Revisited Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir
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Prediction with Advice of Unknown Number of Experts Alexey V. Chernov, Vladimir Vovk
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Primal View on Belief Propagation Tomás Werner
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Probabilistic Similarity Logic Matthias Bröcheler, Lilyana Mihalkova, Lise Getoor
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RAPID: A Reachable Anytime Planner for Imprecisely-Sensed Domains Emma Brunskill, Stuart Russell
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Real-Time Scheduling via Reinforcement Learning Robert Glaubius, Terry Tidwell, Christopher D. Gill, William D. Smart
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Regularized Maximum Likelihood for Intrinsic Dimension Estimation Mithun Das Gupta, Thomas S. Huang
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Risk Sensitive Path Integral Control Bart van den Broek, Wim Wiegerinck, Hilbert J. Kappen
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Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost Ping Li
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Robust Metric Learning by Smooth Optimization Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu
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Rollout Sampling Policy Iteration for Decentralized POMDPs Feng Wu, Shlomo Zilberstein, Xiaoping Chen
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Solving Hybrid Influence Diagrams with Deterministic Variables Yijing Li, Prakash P. Shenoy
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Solving Multistage Influence Diagrams Using Branch-and-Bound Search Changhe Yuan, XiaoJian Wu, Eric A. Hansen
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Source Separation and Higher-Order Causal Analysis of MEG and EEG Kun Zhang, Aapo Hyvärinen
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Sparse-Posterior Gaussian Processes for General Likelihoods Yuan (Alan) Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka
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Speeding up the Binary Gaussian Process Classification Jarno Vanhatalo, Aki Vehtari
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Super-Samples from Kernel Herding Yutian Chen, Max Welling, Alexander J. Smola
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The Cost of Troubleshooting Cost Clusters with Inside Information Thorsten J. Ottosen, Finn Verner Jensen
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The Hierarchical Dirichlet Process Hidden Semi-Markov Model Matthew J. Johnson, Alan S. Willsky
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Three New Sensitivity Analysis Methods for Influence Diagrams Debarun Bhattacharjya, Ross D. Shachter
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Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream Amr Ahmed, Eric P. Xing
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Truthful Feedback for Sanctioning Reputation Mechanisms Jens Witkowski
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Understanding Sampling Style Adversarial Search Methods Raghuram Ramanujan, Ashish Sabharwal, Bart Selman
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Variance-Based Rewards for Approximate Bayesian Reinforcement Learning Jonathan Sorg, Satinder Singh, Richard L. Lewis
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