Heckerman, David

77 publications

ICMLW 2024 Fast Training Dataset Attribution via In-Context Learning Milad Fotouhi, Mohammad Taha Bahadori, Seyi Feyisetan, Payman Arabshahi, David Heckerman

ICML 2024 Multiply-Robust Causal Change Attribution Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, David Heckerman

ICML 2022 End-to-End Balancing for Causal Continuous Treatment-Effect Estimation Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman

ICLR 2021 Debiasing Concept-Based Explanations with Causal Analysis Mohammad Taha Bahadori, David Heckerman

UAI 2011 Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract) Jennifer Listgarten, Carl Myers Kadie, Eric E. Schadt, David Heckerman

UAI 2008 Continuous Time Dynamic Topic Models Chong Wang, David M. Blei, David Heckerman

UAI 2007 Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach Jennifer Listgarten, David Heckerman

JMLR 2006 Considering Cost Asymmetry in Learning Classifiers Francis R. Bach, David Heckerman, Eric Horvitz

JMLR 2005 An MDP-Based Recommender System Guy Shani, David Heckerman, Ronen I. Brafman

AISTATS 2005 On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers Francis Bach, David Heckerman, Eric Horvitz

NeurIPS 2005 Using ``epitomes'' to Model Genetic Diversity: Rational Design of HIV Vaccine Cocktails Nebojsa Jojic, Vladimir Jojic, Christopher Meek, David Heckerman, Brendan J. Frey

UAI 2004 ARMA Time-Series Modeling with Graphical Models Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek

UAI 2004 Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences Nebojsa Jojic, Vladimir Jojic, David Heckerman

JMLR 2004 Large-Sample Learning of Bayesian Networks Is NP-Hard David Maxwell Chickering, David Heckerman, Christopher Meek

UAI 2003 Large-Sample Learning of Bayesian Networks Is NP-Hard David Maxwell Chickering, Christopher Meek, David Heckerman

AISTATS 2003 Learning Bayesian Networks from Dependency Networks: A Preliminary Study Geoff Hulten, David Maxwell Chickering, David Heckerman

UAI 2002 An MDP-Based Recommender System Guy Shani, Ronen I. Brafman, David Heckerman

UAI 2002 CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence Carl Myers Kadie, Christopher Meek, David Heckerman

UAI 2002 Staged Mixture Modelling and Boosting Christopher Meek, Bo Thiesson, David Heckerman

JMLR 2002 The Learning-Curve Sampling Method Applied to Model-Based Clustering Christopher Meek, Bo Thiesson, David Heckerman

MLJ 2001 Accelerating EM for Large Databases Bo Thiesson, Christopher Meek, David Heckerman

MLJ 2001 An Experimental Comparison of Model-Based Clustering Methods Marina Meila, David Heckerman

AISTATS 2001 Learning Mixtures of Smooth, Nonuniform Deformation Models for Probabilistic Image Matching Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman

ICCV 2001 Separating Appearance from Deformation Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman

AISTATS 2001 The Learning Curve Method Applied to Clustering Christopher Meek, Bo Thiesson, David Heckerman

UAI 2000 A Decision Theoretic Approach to Targeted Advertising David Maxwell Chickering, David Heckerman

UAI 2000 Dependency Networks for Collaborative Filtering and Data Visualization David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie

JMLR 2000 Dependency Networks for Inference, Collaborative Filtering, and Data Visualization David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie

UAI 1999 Fast Learning from Sparse Data David Maxwell Chickering, David Heckerman

AISTATS 1999 On the Geometry of DAG Models with Hidden Variables Dan Geiger, David Heckerman, Henry King, Christopher Meek

UAI 1999 Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions Dan Geiger, David Heckerman

UAI 1998 An Experimental Comparison of Several Clustering and Initialization Methods Marina Meila, David Heckerman

UAI 1998 Empirical Analysis of Predictive Algorithms for Collaborative Filtering John S. Breese, David Heckerman, Carl Myers Kadie

UAI 1998 Inferring Informational Goals from Free-Text Queries: A Bayesian Approach David Heckerman, Eric Horvitz

UAI 1998 Learning Mixtures of DAG Models Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman

UAI 1998 The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users Eric Horvitz, Jack S. Breese, David Heckerman, David Hovel, Koos Rommelse

UAI 1997 A Bayesian Approach to Learning Bayesian Networks with Local Structure David Maxwell Chickering, David Heckerman, Christopher Meek

AISTATS 1997 A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection David Heckerman, David Maxwell Chickering

IJCAI 1997 Challenge: What Is the Impact of Bayesian Networks on Learning? Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart Russell

MLJ 1997 Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables David Maxwell Chickering, David Heckerman

UAI 1997 Models and Selection Criteria for Regression and Classification David Heckerman, Christopher Meek

NeCo 1997 Probabilistic Independence Networks for Hidden Markov Probability Models Padhraic Smyth, David Heckerman, Michael I. Jordan

UAI 1997 Structure and Parameter Learning for Causal Independence and Causal Interaction Models Christopher Meek, David Heckerman

UAI 1996 Asymptotic Model Selection for Directed Networks with Hidden Variables Dan Geiger, David Heckerman, Christopher Meek

UAI 1996 Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment John S. Breese, David Heckerman

UAI 1996 Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network David Maxwell Chickering, David Heckerman

UAI 1995 A Bayesian Approach to Learning Causal Networks David Heckerman

UAI 1995 A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks Dan Geiger, David Heckerman

AISTATS 1995 A Decision-Based View of Causality David Heckerman, Ross Schachter

UAI 1995 A Definition and Graphical Representation for Causality David Heckerman, Ross D. Shachter

AISTATS 1995 Decision-Theoretic Case-Based Reasoning John S. Breese, David Heckerman

JAIR 1995 Decision-Theoretic Foundations for Causal Reasoning David Heckerman, Ross D. Shachter

UAI 1995 Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains David Heckerman, Dan Geiger

AISTATS 1995 Learning Bayesian Networks: Search Methods and Experimental Results David Maxwell Chickering, Dan Geiger, David Heckerman

MLJ 1995 Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David Heckerman, Dan Geiger, David Maxwell Chickering

ICML 1995 Learning with Bayesian Networks (Abstract) David Heckerman

UAI 1994 A Decision-Based View of Causality David Heckerman, Ross D. Shachter

UAI 1994 A New Look at Causal Independence David Heckerman, John S. Breese

UAI 1994 Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David Heckerman, Dan Geiger, David Maxwell Chickering

UAI 1994 Learning Gaussian Networks Dan Geiger, David Heckerman

UAI 1993 Causal Independence for Knowledge Acquisition and Inference David Heckerman

UAI 1993 Diagnosis of Multiple Faults: A Sensitivity Analysis David Heckerman, Michael Shwe

UAI 1993 Inference Algorithms for Similarity Networks Dan Geiger, David Heckerman

UAI 1993 UAI '93: Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence, the Catholic University of America, Providence, Washington, DC, USA, July 9-11, 1993 David Heckerman, E. H. Mamdani

UAI 1990 A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System Henri Jacques Suermondt, Gregory F. Cooper, David Heckerman

UAI 1990 Problem Formulation as the Reduction of a Decision Model David Heckerman, Eric Horvitz

UAI 1990 Separable and Transitive Graphoids Dan Geiger, David Heckerman

UAI 1990 Similarity Networks for the Construction of Multiple-Faults Belief Networks David Heckerman

IJCAI 1989 Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study Eric Horvitz, Gregory F. Cooper, David Heckerman

UAI 1987 A Bayesian Perspective on Confidence David Heckerman, Holly Brügge Jimison

AAAI 1987 On the Expressiveness of Rule-Based Systems for Reasoning with Uncertainty David Heckerman, Eric Horvitz

UAI 1986 A Backwards View for Assessment Ross D. Shachter, David Heckerman

AAAI 1986 A Framework for Comparing Alternative Formalisms for Plausible Reasoning Eric Horvitz, David Heckerman, Curtis P. Langlotz

UAI 1986 An Axiomatic Framework for Belief Updates David Heckerman

UAI 1986 The Myth of Modularity in Rule-Based Systems for Reasoning with Uncertainty David Heckerman, Eric Horvitz

UAI 1985 Probabilistic Interpretation for MYCIN's Certainty Factors David Heckerman

UAI 1985 The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research Eric Horvitz, David Heckerman