ML Anthology

UAI 2001

71 papers

A Bayesian Approach to Tackling Hard Computational Problems Eric Horvitz, Yongshao Ruan, Carla P. Gomes, Henry A. Kautz, Bart Selman, David Maxwell Chickering
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A Bayesian Multiresolution Independence Test for Continuous Variables Dimitris Margaritis, Sebastian Thrun
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A Calculus for Causal Relevance Blai Bonet
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A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application Ann E. Nicholson, Tal Boneh, Tim A. Wilkin, Kaye Stacey, Liz Sonenberg, Vicki Steinle
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A Clustering Approach to Solving Large Stochastic Matching Problems Milos Hauskrecht, Eli Upfal
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A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory Phan Hong Giang, Prakash P. Shenoy
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A Dynamic Programming Model for Determining Bidding Strategies in Sequential Auctions: Quasi-Linear Utility and Budget Constraints Hiromitsu Hattori, Makoto Yokoo, Yuko Sakurai, Toramatsu Shintani
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A Factorized Variational Technique for Phase Unwrapping in Markov Random Field Kannan Achan, Brendan J. Frey, Ralf Koetter
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A Logic for Reasoning About Upper Probabilities Joseph Y. Halpern, Riccardo Pucella
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A Mixed Graphical Model for Rhythmic Parsing Christopher Raphael
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A Tractable POMDP for Dynamic Sequencing with Applications to Personalized Internet Content Provision Paat Rusmevichientong, Benjamin Van Roy
Aggregating Learned Probabilistic Beliefs Pedrito Maynard-Reid Ii, Urszula Chajewska
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Analysing Sensitivity Data from Probabilistic Networks Linda C. van der Gaag, Silja Renooij
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Approximating MAP Using Local Search James D. Park, Adnan Darwiche
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Bayesian Error-Bars for Belief Net Inference Tim Van Allen, Russell Greiner, Peter Hooper
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Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation Max Welling, Yee Whye Teh
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Causal Discovery from Changes Jin Tian, Judea Pearl
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Causes and Explanations: A Structural-Model Approach: Part 1: Causes Joseph Y. Halpern, Judea Pearl
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Classifier Learning with Supervised Marginal Likelihood Petri Kontkanen, Petri Myllymäki, Henry Tirri
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Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models Robert G. Cowell
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Confidence Inference in Bayesian Networks Jian Cheng, Marek J. Druzdzel
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Cross-Covariance Modelling via DAGs with Hidden Variables Jacob A. Wegelin, Thomas S. Richardson
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Decision-Theoretic Planning with Concurrent Temporally Extended Actions Khashayar Rohanimanesh, Sridhar Mahadevan
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Direct and Indirect Effects Judea Pearl
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Discovering Multiple Constraints That Are Frequently Approximately Satisfied Geoffrey E. Hinton, Yee Whye Teh
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Efficient Approximation for Triangulation of Minimum Treewidth Eyal Amir
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Efficient Stepwise Selection in Decomposable Models Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan
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Enumerating Markov Equivalence Classes of Acyclic Digraph Models Steven B. Gillispie, Michael D. Perlman
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Estimating Well-Performing Bayesian Networks Using Bernoulli Mixtures Geoff A. Jarrad
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Exact Inference in Networks with Discrete Children of Continuous Parents Uri Lerner, Eran Segal, Daphne Koller
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Expectation Propagation for Approximate Bayesian Inference Thomas P. Minka
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Graphical Models for Game Theory Michael J. Kearns, Michael L. Littman, Satinder Singh
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Graphical Readings of Possibilistic Logic Bases Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade
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Hybrid Processing of Beliefs and Constraints Rina Dechter, David Larkin
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Hypothesis Management in Situation-Specific Network Construction Kathryn B. Laskey, Suzanne M. Mahoney, Ed Wright
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Improved Learning of Bayesian Networks Tomás Kocka, Robert Castelo
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Incorporating Expressive Graphical Models in VariationalApproximations: Chain-Graphs and Hidden Variables Tal El-Hay, Nir Friedman
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Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms Uri Lerner, Ronald Parr
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Instrumentality Tests Revisited Blai Bonet
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Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk John D. Lafferty, Larry A. Wasserman
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Lattice Particle Filters Dirk Ormoneit, Christiane Lemieux, David J. Fleet
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Learning the Dimensionality of Hidden Variables Gal Elidan, Nir Friedman
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Linearity Properties of Bayes Nets with Binary Variables David Danks, Clark Glymour
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Markov Chain Monte Carlo Using Tree-Based Priors on Model Structure Nicos Angelopoulos, James Cussens
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Maximum Likelihood Bounded Tree-Width Markov Networks Nathan Srebro
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Multivariate Information Bottleneck Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
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On Characterizing Inclusion of Bayesian Networks Tomás Kocka, Remco R. Bouckaert, Milan Studený
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Planning and Acting Under Uncertainty: A New Model for Spoken Dialogue System Bo Zhang, Qingsheng Cai, Jianfeng Mao, Baining Guo
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Plausible Reasoning from Spatial Observations Jérôme Lang, Philippe Muller
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Policy Improvement for POMDPs Using Normalized Importance Sampling Christian R. Shelton
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Pre-Processing for Triangulation of Probabilistic Networks Hans L. Bodlaender, Arie M. C. A. Koster, Frank van den Eijkhof, Linda C. van der Gaag
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Probabilistic Logic Programming Under Inheritance with Overriding Thomas Lukasiewicz
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Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments Alexandrin Popescul, Lyle H. Ungar, David M. Pennock, Steve Lawrence
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Recognition Networks for Approximate Inference in BN20 Networks Quaid Morris
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Robust Combination of Local Controllers Carlos Guestrin, Dirk Ormoneit
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Semi-Instrumental Variables: A Test for Instrument Admissibility Tianjiao Chu, Richard Scheines, Peter Spirtes
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Similarity Measures on Preference Structures, Part II: Utility Functions Vu A. Ha, Peter Haddawy, John Miyamoto
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Solving Influence Diagrams Using HUGIN, Shafer-Shenoy and Lazy Propagation Anders L. Madsen, Dennis Nilsson
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Statistical Modeling in Continuous Speech Recognition (CSR) Steve J. Young
Sufficiency, Separability and Temporal Probabilistic Models Avi Pfeffer
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Symmetric Collaborative Filtering Using the Noisy Sensor Model Rita Sharma, David Poole
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The Factored Frontier Algorithm for Approximate Inference in DBNs Kevin P. Murphy, Yair Weiss
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The Optimal Reward Baseline for Gradient-Based Reinforcement Learning Lex Weaver, Nigel Tao
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Toward General Analysis of Recursive Probability Models Daniel Pless, George F. Luger
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UCP-Networks: A Directed Graphical Representation of Conditional Utilities Craig Boutilier, Fahiem Bacchus, Ronen I. Brafman
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Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions Gary A. Davis
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Using Temporal Data for Making Recommendations Andrew Zimdars, David Maxwell Chickering, Christopher Meek
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Value-Directed Sampling Methods for POMDPs Pascal Poupart, Luis E. Ortiz, Craig Boutilier
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Variational MCMC Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Stuart Russell
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Vector-Space Analysis of Belief-State Approximation for POMDPs Pascal Poupart, Craig Boutilier
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When Do Numbers Really Matter? Hei Chan, Adnan Darwiche
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