David Heckerman

199 papers A* 14A 58B 2Journal 111Unranked 6

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Fast Training Dataset Attribution via In-Context Learning. CoRR Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan, Payman Arabshahi, David Heckerman
2024	A*	`conf`	Multiply-Robust Causal Change Attribution. ICML Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, David Heckerman
2024	J	`jnl`	Multiply-Robust Causal Change Attribution. CoRR Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman
2024	J	`jnl`	Removing Spurious Correlation from Neural Network Interpretations. CoRR Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan, Payman Arabshahi, David Heckerman
2023	J	`jnl`	Heckerthoughts. CoRR David Heckerman
2022	A*	`conf`	End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. ICML Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman
2022	—	`ch.`	Why Did They Do That? Probabilistic and Causal Inference Ross D. Shachter, David Heckerman
2021	A*	`conf`	Debiasing Concept-based Explanations with Causal Analysis. ICLR Mohammad Taha Bahadori, David Heckerman
2021	J	`jnl`	Likelihoods and Parameter Priors for Bayesian Networks. CoRR David Heckerman, Dan Geiger
2021	J	`jnl`	Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions. CoRR Dan Geiger, David Heckerman
2020	J	`jnl`	A Tutorial on Learning With Bayesian Networks. CoRR David Heckerman
2019	J	`jnl`	Embedded Bayesian Network Classifiers. CoRR David Heckerman, Christopher Meek
2019	A*	`conf`	Exploiting High Dimensionality in Big Data. KDD David Heckerman
2019	J	`jnl`	Probabilistic Similarity Networks. CoRR David Heckerman
2019	J	`jnl`	Toward Accounting for Hidden Common Causes When Inferring Cause and Effect from Observational Data. ACM Trans. Intell. Syst. Technol. David Heckerman
2018	J	`jnl`	Accounting for hidden common causes when inferring cause and effect from observational data. CoRR David Heckerman
2018	J	`jnl`	Ensembles of Lasso Screening Rules. IEEE Trans. Pattern Anal. Mach. Intell. Seunghak Lee, Nico Görnitz, Eric P. Xing, David Heckerman, Christoph Lippert
2016	J	`jnl`	Dependence and Relevance: A probabilistic view. CoRR Dan Geiger, David Heckerman
2015	J	`jnl`	Computational and statistical issues in personalized medicine. XRDS Christoph Lippert, David Heckerman
2015	J	`jnl`	ConPADE: Genome Assembly Ploidy Estimation from Next-Generation Sequencing Data. PLoS Comput. Biol. Gabriel Rodrigues Alves Margarido, David Heckerman
2014	J	`jnl`	Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinform. Christoph Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl Myers Kadie, David Heckerman, Jennifer Listgarten
2014	J	`jnl`	Literome: PubMed-scale genomic knowledge base in the cloud. Bioinform. Hoifung Poon, Chris Quirk, Charlie DeZiel, David Heckerman
2014	J	`jnl`	Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research. CoRR Eric Horvitz, David Heckerman
2014	J	`jnl`	Variations on undirected graphical models and their relationships. Kybernetika David Heckerman, Christopher Meek, Thomas S. Richardson
2013	J	`jnl`	A Backwards View for Assessment CoRR Ross D. Shachter, David Heckerman
2013	J	`jnl`	A Bayesian Approach to Learning Bayesian Networks with Local Structure CoRR David Maxwell Chickering, David Heckerman, Christopher Meek
2013	J	`jnl`	A Bayesian Approach to Learning Causal Networks CoRR David Heckerman
2013	J	`jnl`	A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks CoRR Dan Geiger, David Heckerman
2013	J	`jnl`	A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System CoRR Henri Jacques Suermondt, Gregory F. Cooper, David Heckerman
2013	J	`jnl`	A Decision Theoretic Approach to Targeted Advertising CoRR David Maxwell Chickering, David Heckerman
2013	J	`jnl`	A Decision-Based View of Causality CoRR David Heckerman, Ross D. Shachter
2013	J	`jnl`	A Definition and Graphical Representation for Causality CoRR David Heckerman, Ross D. Shachter
2013	J	`jnl`	A New Look at Causal Independence CoRR David Heckerman, John S. Breese
2013	J	`jnl`	A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition CoRR David Heckerman, Holly Brügge Jimison
2013	J	`jnl`	A Tractable Inference Algorithm for Diagnosing Multiple Diseases CoRR David Heckerman
2013	J	`jnl`	A powerful and efficient set test for genetic markers that handles confounders. Bioinform. Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl Myers Kadie, David Heckerman
2013	J	`jnl`	Advances in Probabilistic Reasoning CoRR Dan Geiger, David Heckerman
2013	J	`jnl`	An Approximate Nonmyopic Computation for Value of Information CoRR David Heckerman, Eric Horvitz, Blackford Middleton
2013	J	`jnl`	An Axiomatic Framework for Belief Updates CoRR David Heckerman
2013	J	`jnl`	An Empirical Comparison of Three Inference Methods CoRR David Heckerman
2013	J	`jnl`	An Experimental Comparison of Several Clustering and Initialization Methods CoRR Marina Meila, David Heckerman
2013	J	`jnl`	An MDP-based Recommender System CoRR Guy Shani, Ronen I. Brafman, David Heckerman
2013	J	`jnl`	Asymptotic Model Selection for Directed Networks with Hidden Variables CoRR Dan Geiger, David Heckerman, Christopher Meek
2013	J	`jnl`	CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence CoRR Carl Myers Kadie, Christopher Meek, David Heckerman
2013	J	`jnl`	Causal Independence for Knowledge Acquisition and Inference CoRR David Heckerman
2013	J	`jnl`	Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment CoRR John S. Breese, David Heckerman
2013	J	`jnl`	Dependency Networks for Collaborative Filtering and Data Visualization CoRR David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie
2013	J	`jnl`	Diagnosis of Multiple Faults: A Sensitivity Analysis CoRR David Heckerman, Michael Shwe
2013	J	`jnl`	Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network CoRR David Maxwell Chickering, David Heckerman
2013	J	`jnl`	Empirical Analysis of Predictive Algorithms for Collaborative Filtering CoRR John S. Breese, David Heckerman, Carl Myers Kadie
2013	J	`jnl`	Fast Learning from Sparse Data CoRR David Maxwell Chickering, David Heckerman
2013	J	`jnl`	Inference Algorithms for Similarity Networks CoRR Dan Geiger, David Heckerman
2013	J	`jnl`	Inferring Informational Goals from Free-Text Queries: A Bayesian Approach CoRR David Heckerman, Eric Horvitz
2013	J	`jnl`	Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains CoRR David Heckerman, Dan Geiger
2013	J	`jnl`	Learning Bayesian Networks: The Combination of Knowledge and Statistical Data CoRR David Heckerman, Dan Geiger, David Maxwell Chickering
2013	J	`jnl`	Learning Gaussian Networks CoRR Dan Geiger, David Heckerman
2013	J	`jnl`	Learning Mixtures of DAG Models CoRR Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman
2013	J	`jnl`	Models and Selection Criteria for Regression and Classification CoRR David Heckerman, Christopher Meek
2013	J	`jnl`	Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions CoRR Dan Geiger, David Heckerman
2013	J	`jnl`	Practical and Theoretical Advances in Knowledge Acquisition of Probabilistic Networks CoRR Dan Geiger, David Heckerman
2013	J	`jnl`	Probabilistic Interpretations for MYCIN's Certainty Factors CoRR David Heckerman
2013	J	`jnl`	Problem Formulation as the Reduction of a Decision Model CoRR David Heckerman, Eric Horvitz
2013	J	`jnl`	Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993) CoRR David Heckerman, E. H. Mamdani
2013	J	`jnl`	Similarity Networks for the Construction of Multiple-Faults Belief Networks CoRR David Heckerman
2013	J	`jnl`	Staged Mixture Modelling and Boosting CoRR Christopher Meek, Bo Thiesson, David Heckerman
2013	J	`jnl`	Structure and Parameter Learning for Causal Independence and Causal Interaction Models CoRR Christopher Meek, David Heckerman
2013	J	`jnl`	The Compilation of Decision Models CoRR David Heckerman, John S. Breese, Eric Horvitz
2013	J	`jnl`	The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users CoRR Eric Horvitz, John S. Breese, David Heckerman, David Hovel, Koos Rommelse
2013	J	`jnl`	The Myth of Modularity in Rule-Based Systems CoRR David Heckerman, Eric Horvitz
2013	J	`jnl`	The Role of Calculi in Uncertain Inference Systems CoRR Michael P. Wellman, David Heckerman
2012	J	`jnl`	ARMA Time-Series Modeling with Graphical Models CoRR Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek
2012	J	`jnl`	Continuous Time Dynamic Topic Models CoRR Chong Wang, David M. Blei, David Heckerman
2012	J	`jnl`	Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach CoRR Jennifer Listgarten, David Heckerman
2012	—	`conf`	Inferring novel associations between SNP sets and gene sets in eQTL study using sparse graphical model. BCB Wei Cheng, Xiang Zhang, Yubao Wu, Xiaolin Yin, Jing Li, David Heckerman, Wei Wang
2012	J	`jnl`	Joint discovery of haplotype blocks and complex trait associations from SNP sequences CoRR Nebojsa Jojic, Vladimir Jojic, David Heckerman
2012	J	`jnl`	Large-Sample Learning of Bayesian Networks is NP-Hard CoRR David Maxwell Chickering, Christopher Meek, David Heckerman
2011	A	`conf`	Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract). UAI Jennifer Listgarten, Carl Myers Kadie, Eric E. Schadt, David Heckerman
2009	—	`ch.`	Healthcare delivery in developing countries: challenges and potential solutions. The Fourth Paradigm Joel Robertson, Del DeHart, Kristin M. Tolle, David Heckerman
2009	J	`jnl`	PhyloDet: a scalable visualization tool for mapping multiple traits to large evolutionary trees. Bioinform. Bongshin Lee, Lev Nachmanson, George G. Robertson, Jonathan M. Carlson, David Heckerman
2008	—	`ch.`	A Tutorial on Learning with Bayesian Networks. Innovations in Bayesian Networks David Heckerman
2008	J	`jnl`	Comparison of Immunogen Designs That Optimize Peptide Coverage: Reply to Fischer et al. PLoS Comput. Biol. David C. Nickle, Nebojsa Jojic, David Heckerman, Vladimir Jojic, Darko Kirovski, Morgane Rolland, Sergei L. Kosakovsky Pond, James I. Mullins
2008	A	`conf`	Continuous Time Dynamic Topic Models. UAI Chong Wang, David M. Blei, David Heckerman
2008	J	`jnl`	Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 Gag. PLoS Comput. Biol. Jonathan M. Carlson, Zabrina L. Brumme, Christine M. Rousseau, Chanson J. Brumme, Philippa Matthews, Carl Myers Kadie, James I. Mullins, Bruce D. Walker, P. Richard Harrigan, Philip J. R. Goulder, David Heckerman
2008	J	`jnl`	Shift-Invariant Adaptive Double Threading: Learning MHC II-Peptide Binding. J. Comput. Biol. Noah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic
2008	J	`jnl`	Statistical Resolution of Ambiguous HLA Typing Data. PLoS Comput. Biol. Jennifer Listgarten, Zabrina L. Brumme, Carl Myers Kadie, Gao Xiaojiang, Bruce D. Walker, Mary Carrington, Philip J. R. Goulder, David Heckerman
2007	J	`jnl`	A Statistical Framework for Modeling HLA-Dependent T Cell Response Data. PLoS Comput. Biol. Jennifer Listgarten, Nicole Frahm, Carl Myers Kadie, Christian Brander, David Heckerman
2007	J	`jnl`	Coping with Viral Diversity in HIV Vaccine Design. PLoS Comput. Biol. David C. Nickle, Morgane Rolland, Mark A. Jensen, Sergei L. Kosakovsky Pond, Wenjie Deng, Mark Seligman, David Heckerman, James I. Mullins, Nebojsa Jojic
2007	A	`conf`	Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach. UAI Jennifer Listgarten, David Heckerman
2007	J	`jnl`	Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. J. Comput. Biol. David Heckerman, Carl Myers Kadie, Jennifer Listgarten
2007	B	`conf`	Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide Binding. RECOMB Noah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic
2007	J	`jnl`	Spam and the ongoing battle for the inbox. Commun. ACM Joshua Goodman, Gordon V. Cormack, David Heckerman
2006	J	`jnl`	Considering Cost Asymmetry in Learning Classifiers. J. Mach. Learn. Res. Francis R. Bach, David Heckerman, Eric Horvitz
2006	—	`conf`	Learning MHC I - peptide binding. ISMB (Supplement of Bioinformatics) Nebojsa Jojic, Manuel Reyes-Gomez, David Heckerman, Carl Myers Kadie, Ora Schueler-Furman
2006	B	`conf`	Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. RECOMB David Heckerman, Carl Myers Kadie, Jennifer Listgarten
2005	J	`jnl`	An MDP-Based Recommender System. J. Mach. Learn. Res. Guy Shani, David Heckerman, Ronen I. Brafman
2005	A	`conf`	On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers. AISTATS Francis R. Bach, David Heckerman, Eric Horvitz
2005	J	`jnl`	The First Conference on E-mail and Anti-Spam. AI Mag. David Heckerman, Tom Berson, Joshua Goodman, Andrew Y. Ng
2005	—	`conf`	Using epitomes to model genetic diversity: Rational design of HIV vaccines. NIPS Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman
2004	A	`conf`	ARMA Time-Series Modeling with Graphical Models. UAI Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek
2004	—	`conf`	Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) Vladimir Jojic, Nebojsa Jojic, Christopher Meek, Dan Geiger, Adam C. Siepel, David Haussler, David Heckerman
2004	A*	`conf`	Graphical models for data mining. KDD David Heckerman
2004	A	`conf`	Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences. UAI Nebojsa Jojic, Vladimir Jojic, David Heckerman
2004	J	`jnl`	Large-Sample Learning of Bayesian Networks is NP-Hard. J. Mach. Learn. Res. David Maxwell Chickering, David Heckerman, Christopher Meek
2003	A	`conf`	Large-Sample Learning of Bayesian Networks is NP-Hard. UAI David Maxwell Chickering, Christopher Meek, David Heckerman
2003	A	`conf`	Learning Bayesian Networks From Dependency Networks: A Preliminary Study. AISTATS Geoff Hulten, David Maxwell Chickering, David Heckerman
2003	J	`jnl`	Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Min. Knowl. Discov. Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White
2003	A*	`conf`	Recommendation as a Stochastic Sequential Decision Problem. ICAPS Ronen I. Brafman, David Heckerman, Guy Shani
2003	J	`jnl`	Targeted Advertising on the Web with Inventory Management. Interfaces David Maxwell Chickering, David Heckerman
2002	A	`conf`	An MDP-based Recommender System. UAI Guy Shani, Ronen I. Brafman, David Heckerman
2002	A	`conf`	Autoregressive Tree Models for Time-Series Analysis. SDM Christopher Meek, David Maxwell Chickering, David Heckerman
2002	A	`conf`	CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence. UAI Carl Myers Kadie, Christopher Meek, David Heckerman
2002	A	`conf`	Staged Mixture Modelling and Boosting. UAI Christopher Meek, Bo Thiesson, David Heckerman
2002	J	`jnl`	The Learning-Curve Sampling Method Applied to Model-Based Clustering. J. Mach. Learn. Res. Christopher Meek, Bo Thiesson, David Heckerman
2001	J	`jnl`	Accelerating EM for Large Databases. Mach. Learn. Bo Thiesson, Christopher Meek, David Heckerman
2001	J	`jnl`	An Experimental Comparison of Model-Based Clustering Methods. Mach. Learn. Marina Meila, David Heckerman
2001	A	`conf`	Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching. AISTATS Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman
2001	A*	`conf`	Separating Appearance from Deformation. ICCV Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman
2001	J	`jnl`	Statistical Models for Data Mining. Data Min. Knowl. Discov. Paolo Giudici, David Heckerman, Joe Whittaker
2001	A	`conf`	The Learning Curve Method Applied to Clustering. AISTATS Christopher Meek, Bo Thiesson, David Heckerman
2000	A	`conf`	A Decision Theoretic Approach to Targeted Advertising. UAI David Maxwell Chickering, David Heckerman
2000	J	`jnl`	A comparison of scientific and engineering criteria for Bayesian model selection. Stat. Comput. David Maxwell Chickering, David Heckerman
2000	A	`conf`	Dependency Networks for Collaborative Filtering and Data Visualization. UAI David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie
2000	J	`jnl`	Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. J. Mach. Learn. Res. David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie
2000	A*	`conf`	Targeted advertising with inventory management. EC David Maxwell Chickering, David Heckerman
2000	A*	`conf`	Visualization of navigation patterns on a Web site using model-based clustering. KDD Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White
1999	A	`conf`	Fast Learning from Sparse Data. UAI David Maxwell Chickering, David Heckerman
1999	A	`conf`	On the geometry of DAG models with hidden variables. AISTATS Dan Geiger, David Heckerman, Henry King, Christopher Meek
1999	A	`conf`	Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions. UAI Dan Geiger, David Heckerman
1999	A	`ed.`	Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, AISTATS 1999, Fort Lauderdale, Florida, USA, January 3-6, 1999 AISTATS David Heckerman, Joe Whittaker
1998	—	`ch.`	A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models David Heckerman
1998	A	`conf`	An Experimental Comparison of Several Clustering and Initialization Methods. UAI Marina Meila, David Heckerman
1998	—	`ch.`	Asymptotic Model Selection for Directed Networks with Hidden Variables. Learning in Graphical Models Dan Geiger, David Heckerman, Christopher Meek
1998	A	`conf`	Empirical Analysis of Predictive Algorithms for Collaborative Filtering. UAI John S. Breese, David Heckerman, Carl Myers Kadie
1998	A	`conf`	Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. UAI David Heckerman, Eric Horvitz
1998	A	`conf`	Learning Mixtures of DAG Models. UAI Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman
1998	J	`jnl`	Probabilistic relevance relations. IEEE Trans. Syst. Man Cybern. Part A Dan Geiger, David Heckerman
1998	A	`conf`	The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. UAI Eric Horvitz, Jack S. Breese, David Heckerman, David Hovel, Koos Rommelse
1997	A	`conf`	A Bayesian Approach to Learning Bayesian Networks with Local Structure. UAI David Maxwell Chickering, David Heckerman, Christopher Meek
1997	A	`conf`	A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection. AISTATS David Heckerman, David Maxwell Chickering
1997	J	`jnl`	Bayesian Networks for Data Mining. Data Min. Knowl. Discov. David Heckerman
1997	—	`conf`	Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart Russell
1997	J	`jnl`	Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables. Mach. Learn. David Maxwell Chickering, David Heckerman
1997	A	`conf`	Models and Selection Criteria for Regression and Classification. UAI David Heckerman, Christopher Meek
1997	J	`jnl`	Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Comput. Padhraic Smyth, David Heckerman, Michael I. Jordan
1997	A*	`ed.`	Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997 KDD David Heckerman, Heikki Mannila, Daryl Pregibon
1997	A	`conf`	Structure and Parameter Learning for Causal Independence and Causal Interaction Models. UAI Christopher Meek, David Heckerman
1996	A	`conf`	Asymptotic Model Selection for Directed Networks with Hidden Variables. UAI Dan Geiger, David Heckerman, Christopher Meek
1996	—	`ch.`	Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery and Data Mining David Heckerman
1996	J	`jnl`	Causal independence for probability assessment and inference using Bayesian networks. IEEE Trans. Syst. Man Cybern. Part A David Heckerman, John S. Breese
1996	A	`conf`	Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment. UAI John S. Breese, David Heckerman
1996	J	`jnl`	Decision-theoretic case-based reasoning. IEEE Trans. Syst. Man Cybern. Part A John S. Breese, David Heckerman
1996	A	`conf`	Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. UAI David Maxwell Chickering, David Heckerman
1996	J	`jnl`	Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets. Artif. Intell. Dan Geiger, David Heckerman
1996	—	`ch.`	Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic Introduction. Uncertainty Management in Information Systems Max Henrion, Henri Jacques Suermondt, David Heckerman
1995	A	`conf`	A Bayesian Approach to Learning Causal Networks. UAI David Heckerman
1995	A	`conf`	A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI Dan Geiger, David Heckerman
1995	A	`conf`	A Definition and Graphical Representation for Causality. UAI David Heckerman, Ross D. Shachter
1995	J	`jnl`	Bayesian Networks. Commun. ACM David Heckerman, Michael P. Wellman
1995	J	`jnl`	Decision-Theoretic Foundations for Causal Reasoning. CoRR David Heckerman, Ross D. Shachter
1995	J	`jnl`	Decision-Theoretic Foundations for Causal Reasoning. J. Artif. Intell. Res. David Heckerman, Ross D. Shachter
1995	J	`jnl`	Decision-Theoretic Troubleshooting. Commun. ACM David Heckerman, John S. Breese, Koos Rommelse
1995	J	`jnl`	Editorial: real-world applications of uncertain reasoning. Int. J. Hum. Comput. Stud. David Heckerman, E. H. Mamdani, Michael P. Wellman
1995	A	`conf`	Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI David Heckerman, Dan Geiger
1995	J	`jnl`	Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Mach. Learn. David Heckerman, Dan Geiger, David Maxwell Chickering
1995	A*	`conf`	Learning With Bayesian Networks (Abstract). ICML David Heckerman
1995	J	`jnl`	On Finding a Cycle Basis with a Shortest Maximal Cycle. Inf. Process. Lett. David Maxwell Chickering, Dan Geiger, David Heckerman
1995	J	`jnl`	Real-World Applications of Bayesian Networks - Introduction. Commun. ACM David Heckerman, E. H. Mamdani, Michael P. Wellman
1994	A	`conf`	A Decision-based View of Causality. UAI David Heckerman, Ross D. Shachter
1994	A	`conf`	A New Look at Causal Independence. UAI David Heckerman, John S. Breese
1994	—	`conf`	Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. KDD Workshop David Heckerman, Dan Geiger, David Maxwell Chickering
1994	A	`conf`	Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI David Heckerman, Dan Geiger, David Maxwell Chickering
1994	A	`conf`	Learning Gaussian Networks. UAI Dan Geiger, David Heckerman
1993	J	`jnl`	An Approximate Nonmyopic Computation for Value of Information. IEEE Trans. Pattern Anal. Mach. Intell. David Heckerman, Eric Horvitz, Blackford Middleton
1993	A	`conf`	Causal Independence for Knowledge Acquisition and Inference. UAI David Heckerman
1993	A	`conf`	Diagnosis of Multiple Faults: A Sensitivity Analysis. UAI David Heckerman, Michael Shwe
1993	A	`conf`	Inference Algorithms for Similarity Networks. UAI Dan Geiger, David Heckerman
1993	A	`ed.`	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 UAI David Heckerman, E. H. Mamdani
1992	J	`jnl`	From certainty factors to belief networks. Artif. Intell. Medicine David Heckerman, Edward H. Shortliffe
1991	A	`conf`	Advances in Probabilistic Reasoning. UAI Dan Geiger, David Heckerman
1991	A	`conf`	An Approximate Nonmyopic Computation for Value of Information. UAI David Heckerman, Eric Horvitz, Blackford Middleton
1991	—	`book`	Probabilistic similarity networks. David Heckerman
1990	A	`conf`	A combination of cutset conditioning with clique-tree propagation in the Pathfinder system. UAI Henri Jacques Suermondt, Gregory F. Cooper, David Heckerman
1990	J	`jnl`	Probabilistic similarity networks. Networks David Heckerman
1990	A	`conf`	Problem formulation as the reduction of a decision model. UAI David Heckerman, Eric Horvitz
1990	A	`conf`	Similarity networks for the construction of multiple-faults belief networks. UAI David Heckerman
1990	A	`conf`	separable and transitive graphoids. UAI Dan Geiger, David Heckerman
1989	A	`conf`	A Tractable Inference Algorithm for Diagnosing Multiple Diseases. UAI David Heckerman
1989	A*	`conf`	Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study. IJCAI Eric Horvitz, Gregory F. Cooper, David Heckerman
1988	J	`jnl`	A perspective on confidence and its use in focusing attention during knowledge acquisition. Int. J. Approx. Reason. David Heckerman, Holly Brügge Jimison
1988	A	`conf`	An empirical comparison of three inference methods. UAI David Heckerman
1987	A	`conf`	A Bayesian Perspective on Confidence. UAI David Heckerman, Holly Brügge Jimison
1987	A*	`conf`	On the Expressiveness of Rule-based Systems for Reasoning with Uncertainty. AAAI David Heckerman, Eric Horvitz
1987	J	`jnl`	Thinking Backward for Knowledge Acquisition. AI Mag. Ross D. Shachter, David Heckerman
1986	A*	`conf`	A Framework for Comparing Alternative Formalisms for Plausible Reasoning. AAAI Eric Horvitz, David Heckerman, Curtis P. Langlotz
1986	A	`conf`	A backwards view for assessment. UAI Ross D. Shachter, David Heckerman
1986	A	`conf`	An axiomatic framework for belief updates. UAI David Heckerman
1986	A	`conf`	The myth of modularity in rule-based systems for reasoning with uncertainty. UAI David Heckerman, Eric Horvitz
1985	A	`conf`	Probabilistic Interpretation for MYCIN's Certainty Factors. UAI David Heckerman
1985	A	`conf`	The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research. UAI Eric Horvitz, David Heckerman