David Heckerman - researchr alias

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2009

PhyloDet: a scalable visualization tool for mapping multiple traits to large evolutionary treesBongshin Lee, Lev Nachmanson, George G. Robertson, Jonathan M. Carlson, David Heckerman. bioinformatics, 25(19):2611-2612, 2009. [doi]

2008

A Tutorial on Learning with Bayesian NetworksDavid Heckerman. In Dawn E. Holmes, Lakhmi C. Jain, editors, Innovations in Bayesian Networks: Theory and Applications. Volume 156 of Studies in Computational Intelligence, pages 33-82, Springer, 2008. [doi]

Continuous Time Dynamic Topic ModelsChong Wang, David M. Blei, David Heckerman. uai 2008: 579-586 [doi]

Shift-Invariant Adaptive Double Threading: Learning MHC II-Peptide BindingNoah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic. jcb, 15(7):927-942, 2008. [doi]

2007

Spam and the ongoing battle for the inboxJoshua Goodman, Gordon V. Cormack, David Heckerman. CACM, 50(2):24-33, 2007. [doi]

Leveraging Information Across HLA Alleles/Supertypes Improves Epitope PredictionDavid Heckerman, Carl Myers Kadie, Jennifer Listgarten. jcb, 14(6):736-746, 2007. [doi]

Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide BindingNoah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic. recomb 2007: 181-195 [doi]

2006

2005

2004

2003

Recommendation as a Stochastic Sequential Decision ProblemRonen I. Brafman, David Heckerman, Guy Shani. aips 2003: 164-173

Model-Based Clustering and Visualization of Navigation Patterns on a Web SiteIgor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White. datamine, 7(4):399-424, 2003. [doi]

Large-Sample Learning of Bayesian Networks is NP-HardDavid Maxwell Chickering, Christopher Meek, David Heckerman. uai 2003: 124-133

2002

2001

Statistical Models for Data MiningPaolo Giudici, David Heckerman, Joe Whittaker. datamine, 5(3):163-165, 2001.

Separating Appearance from DeformationNebojsa Jojic, Patrice Simard, Brendan J. Frey, David Heckerman. iccv 2001: 288-294

An Experimental Comparison of Model-Based Clustering MethodsMarina Meila, David Heckerman. ml, 42(1/2):9-29, 2001.

Accelerating EM for Large DatabasesBo Thiesson, Christopher Meek, David Heckerman. ml, 45(3):279-299, 2001.

2000

1999

Fast Learning from Sparse DataDavid Maxwell Chickering, David Heckerman. uai 1999: 109-115 [doi]

1998

1997

Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden VariablesDavid Maxwell Chickering, David Heckerman. ml, 29(2-3):181-212, 1997.

A Bayesian Approach to Learning Bayesian Networks with Local StructureDavid Maxwell Chickering, David Heckerman, Christopher Meek. uai 1997: 80-89 [doi]

Challenge: What is the Impact of Bayesian Networks on Learning?Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart J. Russell. IJCAI 1997: 10-15

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997David Heckerman, Heikki Mannila, Daryl Pregibon, editors, AAAI Press, 1997.

Models and Selection Criteria for Regression and ClassificationDavid Heckerman, Christopher Meek. uai 1997: 223-228 [doi]

Bayesian Networks for Data MiningDavid Heckerman. datamine, 1(1):79-119, 1997.

Structure and Parameter Learning for Causal Independence and Causal Interaction ModelsChristopher Meek, David Heckerman. uai 1997: 366-375 [doi]

Probabilistic Independence Networks for Hidden Markov Probability ModelsPadhraic Smyth, David Heckerman, Michael I. Jordan. neco, 9(2):227-269, 1997. [doi]

1996

Decision-Theoretic Troubleshooting: A Framework for Repair and ExperimentJohn S. Breese, David Heckerman. uai 1996: 124-132 [doi]

Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian NetworkDavid Maxwell Chickering, David Heckerman. uai 1996: 158-168 [doi]

Asymptotic Model Selection for Directed Networks with Hidden VariablesDan Geiger, David Heckerman, Christopher Meek. uai 1996: 283-290 [doi]

Knowledge Representation and Inference in Similarity Networks and Bayesian MultinetsDan Geiger, David Heckerman. ai, 82(1-2):45-74, 1996. [doi]

Bayesian Networks for Knowledge DiscoveryDavid Heckerman. In Advances in Knowledge Discovery and Data Mining. pages 273-305, 1996.

Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic IntroductionMax Henrion, Henri Jacques Suermondt, David Heckerman. In Uncertainty Management in Information Systems. pages 255-284, 1996.

1995

On Finding a Cycle Basis with a Shortest Maximal CycleDavid Maxwell Chickering, Dan Geiger, David Heckerman. ipl, 54(1):55-58, 1995. [doi]

A Characterization of the Dirichlet Distribution with Application to Learning Bayesian NetworksDan Geiger, David Heckerman. uai 1995: 196-207 [doi]

A Definition and Graphical Representation for CausalityDavid Heckerman, Ross D. Shachter. uai 1995: 262-273 [doi]

Decision-Theoretic Foundations for Causal ReasoningDavid Heckerman, Ross D. Shachter. jair, 3:405-430, 1995.

Learning Bayesian Networks: A Unification for Discrete and Gaussian DomainsDavid Heckerman, Dan Geiger. uai 1995: 274-284 [doi]

Learning With Bayesian Networks (Abstract)David Heckerman. icml 1995: 588

Decision-Theoretic TroubleshootingDavid Heckerman, John S. Breese, Koos Rommelse. CACM, 38(3):49-57, 1995.

Bayesian NetworksDavid Heckerman, Michael P. Wellman. CACM, 38(3):27-30, 1995.

Learning Bayesian Networks: The Combination of Knowledge and Statistical DataDavid Heckerman, Dan Geiger, David Maxwell Chickering. ml, 20(3):197-243, 1995.

Real-World Applications of Bayesian Networks - IntroductionDavid Heckerman, E. H. Mamdani, Michael P. Wellman. CACM, 38(3):24-26, 1995.

Editorial: real-world applications of uncertain reasoningDavid Heckerman, E. H. Mamdani, Michael P. Wellman. ijmms, 42(6):573-574, 1995. [doi]

A Bayesian Approach to Learning Causal NetworksDavid Heckerman. uai 1995: 285-295 [doi]

1994

1993

1991

Advances in Probabilistic ReasoningDan Geiger, David Heckerman. uai 1991: 118-126 [doi]

An Approximate Nonmyopic Computation for Value of InformationDavid Heckerman, Eric Horvitz, Blackford Middleton. uai 1991: 135-141 [doi]

Probabilistic similarity networksDavid Heckerman. ACM Doctoral dissertation awards, MIT Press, 1991.

1990

1989

A Tractable Inference Algorithm for Diagnosing Multiple DiseasesDavid Heckerman. uai 1990: 163-172 [doi]

Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical StudyEric Horvitz, Gregory F. Cooper, David Heckerman. IJCAI 1989: 1121-1127

1988

A perspective on confidence and its use in focusing attention during knowledge acquisitionDavid Heckerman, Holly Brügge Jimison. ijar, 2(3):336, 1988.

An empirical comparison of three inference methodsDavid Heckerman. uai 1990: 283-302 [doi]

1987

A Bayesian Perspective on ConfidenceDavid Heckerman, Holly Brügge Jimison. uai 1989: 149-160 [doi]

On the Expressiveness of Rule-based Systems for Reasoning with UncertaintyDavid Heckerman, Eric Horvitz. AAAI 1987: 121-126

Thinking Backward for Knowledge AcquisitionRoss D. Shachter, David Heckerman. aim, 8(3):55-61, 1987.

1986

An axiomatic framework for belief updatesDavid Heckerman. uai 1988: 11-22 [doi]

The myth of modularity in rule-based systems for reasoning with uncertaintyDavid Heckerman, Eric Horvitz. uai 1988: 23-34 [doi]

A Framework for Comparing Alternative Formalisms for Plausible ReasoningEric Horvitz, David Heckerman, Curtis Langlotz. AAAI 1986: 210-214

A backwards view for assessmentRoss D. Shachter, David Heckerman. uai 1988: 317-324 [doi]

1985

Probabilistic Interpretation for MYCIN s Certainty FactorsDavid Heckerman. uai 1988: 167-196 [doi]

The Inconsistent Use of Measures of Certainty in Artificial Intelligence ResearchEric Horvitz, David Heckerman. uai 1988: 137-152 [doi]