Publications: Machine Learning

 

·         D. Heckerman, D. Gurdasani, C. Kadie, C. Pomilla, T. Carstensen, H. Martin, K. Ekoru, R.N. Nsubuga, G. Ssenyomo A. Kamali, P. Kaleebu, C. Widmer, and M.S. Sandhu.  Linear mixed model for heritability estimation that explicitly addresses environmental variation.  PNAS, 113: 7377–7382, July 2016 (doi: 10.1073/pnas.1510497113).

 

·         C. Lippert and D. Heckerman.  Computational and statistical issues in personalized medicine.  XRDS 21, 24-27, Summer 2015 (doi:10.1145/2788502).

 

·         O. Weissbrod, C. Lippert, D. Geiger, and D. Heckerman.  Accurate liability estimation improves power in ascertained case-control studiesNature Methods, Feb 2015 (doi:10.1038/nmeth.3285).

 

·         C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman. Further Improvements to Linear Mixed Models for Genome-Wide Association Studies. Scientific Reports 4, 6874, Nov 2014 (doi:10.1038/srep06874).

 

·         F. Pereyra, D. Heckerman, J. Carlson, C. Kadie, D. Soghoian, D. Karel, A. Goldenthal, O. Davis, C. DeZiel, T. Lin, J. Peng, A. Piechocka, M. Carrington, and B. Walker. HIV Control Is Mediated in Part by CD8+ T-Cell Targeting of Specific Epitopes. J. Virol 88 12937-12948, Aug 2014.

 

·         C. Lippert, J. Xiang, D. Horta, C. Widmer, C.M. Kadie, D. Heckerman, J. Listgarten. Greater Power and Computational Efficiency for Kernel-Based Association Testing of Sets of Genetic Variants. Bioinformatics 30 , July 2014 (doi: 10.1093/bioinformatics/btu504).

 

·         J. Kuipers, G. Moffa, and D. Heckerman. Addendum on the scoring of Gaussian directed acyclic graphical models. Annals of Statistics 42, 1689-1691, Aug 2014.

 

·         D. Heckerman, C. Meek, and T. Richardson. Variations on undirected graphical models and their relationships. Kybernetika 50, 363–377, July 2014.

 

·         N. Furlotte, D. Heckerman, and C. Lippert.  Quantifying the uncertainty in heritabilityJournal of Human Genetics 27, March 2014 (doi: 10.1038/jhg.2014.15).

 

·         J. Zou, C. Lippert, D. Heckerman, M. Aryee, and J. Listgarten.  Epigenome-wide association studies without the need for cell-type compositionNature Methods 11:309–311, Jan 2014 (doi:10.1038/nmeth.2815).

 

·         I. Bartha, J.Carlson, C. Brumme, P. McLaren, Z. Brumme, M. John, D. Haas, J. Martinez-Picado, J. Dalmau, C. López-Galíndez, C. Casado, A. Rauch, H. Günthard, E. Bernasconi, P. Vernazza, T. Klimkait, S. Yerly, S. O’Brien, J. Listgarten, N. Pfeifer, C. Lippert, N. Fusi, Z. Kutalik, T. Allen, V. Müller, P. Harrigan, D. Heckerman, A. Telenti, and J. Fellay.  A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral controleLife 2013;2:e01123, October 2013 (http://dx.doi.org/10.7554/eLife.01123).

 

·         C. Lippert, G. Quon, E.Y. Kang, C.M. Kadie, J. Listgarten, and D. Heckerman.  The benefits of selecting phenotype-specific variants for applications of mixed models in genomicsScientific Reports, 3, May 2013 (doi:10.1038/srep01815).

 

·         J. Listgarten, C. Lippert, E.Y. Kang, J. Xiang, C.M. Kadie, and D. Heckerman.  A powerful and efficient set test for genetic markers that handles confoundersBioinformatics, May 2013 (doi: 10.1093/bioinformatics/btt177).

 

·         J. Listgarten, C. Lippert, and D. Heckerman. FaST-LMM-Select for addressing confounding from spatial structure and rare variantsNature Genetics, 45: 470-471, April 2013 (doi:10.1038/ng.2620).

 

·         G. Quon, C. Lippert, D. Heckerman, and J. Listgarten.  Patterns of methylation heritability in a genome-wide analysis of four brain regionsNucleic Acids Research, March 2013 (doi: 10.1093/nar/gks1449).

 

·         C. Lippert, J. Listgarten, R.I. Davidson, J. Baxter, H. Poon, C.M. Kadie, and  D. Heckerman.  An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust DataScientific Reports, 3, Jan 2013 (doi:10.1038/srep01099).

 

·         J. Listgarten, C. Lippert, C.M. Kadie, R.I. Davidson, E. Eskin, and D. Heckerman.  Improved linear mixed models for genome-wide association studiesNature Methods, 9: 525-526, June 2012 (doi:10.1038/nmeth.2037).

 

·         X. Zhang, W. Cheng, J. Listgarten, C. Kadie, S. Huang, W. Wang, and D. Heckerman.  Learning transcriptional regulatory relationships using sparse graphical modelsPLoS One 7(5): e35762, May 2012 (doi:10.1371/journal.pone.0035762).

 

·         A. Renton, et al.  A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTDNeuron 72(2): 257-268, Oct 2011.

 

·         C. Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, and D. Heckerman.  FaST linear mixed models for genome-wide association studiesNature Methods, 8: 833-835, Oct 2011 (doi:10.1038/nmeth.1681).

 

·         J. Listgarten, C. Kadie, E. Schadt, D. Heckerman.  Correction for hidden confounders in the genetic analysis of gene expression. PNAS, 107 (38): 16465-16470, September 2010 (doi: 10.1073/pnas.1002425107).

 

·         R. Shachter and D. Heckerman.  Pearl Causality and the Value of Control.  In Heuristics, Probability and Causality: A Tribute to Judea Pearl, R. Dechter, H. Geffner, and J. Halpern eds.  2010.

 

·         J. Carlson, Z. Brumme, C. Rousseau, C. Brumme, P. Matthews, C. Kadie, J. Mullins, B. Walker, P. Harrigan, P. Goulder, D. Heckerman.  Phylogenetic dependency networks: Inferring patterns of CTL escape and codon covariation in HIV-1 Gag. PLoS Computational Biology, 4(11): e1000225, November 2008.

 

·         C. Wang, D. Blei, and D. Heckerman.  Continuous Time Dynamic Topic Models.  In Proceedings of Twenty Fourth Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, UAI Press, July 2008.  Local copy.

 

·         J. Listgarten, Z. Brumme, C. Kadie, G. Xiaojiang, B. Walker, M. Carrington, P. Goulder, and D. Heckerman.  Statistical resolution of ambiguous HLA typing dataPLoS Computational Biology, 4(2): e1000016, February, 2008.

 

·         J. Listgarten, N. Frahm, C. Kadie, C. Brander, D. Heckerman.  A statistical framework for modeling HLA-dependent T cell response data, PLoS Computational Biology, 3(10): e188, October 2007.

 

·         D. Heckerman, C. Kadie, and J. Listgarten.  Leveraging information across HLA alleles/supertypes improves epitope prediction.  J. of Comp. Bio, 14(6): 736-746, August 2007.  Also appears as MSR-TR-05-127, Microsoft Research, September, 2005.

 

·         J. Carlson, C. Kadie, S. Mallal, and D. Heckerman.  Leveraging hierarchical population structure in discrete association studies. PLoS ONE, 2(7): e591, July 2007.

 

·         J. Listgarten and D. Heckerman, Determining the number of non-spurious arcs in a learned DAG model: Investigation of a Bayesian and a frequentist approach.  In Proceedings of Twenty Third Conference on Uncertainty in Artificial Intelligence, Vancouver, Canada, UAI Press, July 2007.  Also appears as Technical Report MSR-TR-07-60, Microsoft Research, May, 2007.  Local copy.

 

·         D. Nickle, M. Rolland, M. Jensen, S. Pond, W. Deng, M. Seligman, D. Heckerman, J. Mullins, and N. Jojic.  Coping with viral diversity in HIV vaccine design, PLoS Computational Biology, 3(4): e75, April 2007.

 

·         T. Bhattacharya, M. Daniels, D. Heckerman, B. Foley, N. Frahm, C. Kadie, J. Carlson, K. Yusim, B. McMahon, B. Gaschen, S. Mallal, J. Mullins, D. Nickle, J. Herbeck, C. Rousseau, G. Learn, T. Miura, C. Brander, B. Walker, B. Korber.  Founder effects in the assessment of HIV polymorphisms and HLA allele associations, Science, 315, 1583-1586, March 16 2007.

 

·         F. Bach, D. Heckerman, E. Horvitz, Considering cost asymmetry in learning classifiersJournal of Machine Learning Research, 7, 1713-1741, 2006.

 

·         N. Jojic, V. Jojic, B. Frey, C. Meek, and D. Heckerman.  Using epitomes to model genetic diversity: Rational design of HIV vaccine cocktails.  NIPS 2005.

 

·         J. Goodman, D. Heckerman, and R. Rounthwaite.  Stopping SpamScientific American, April, 2005.

 

·         G. Shani, D. Heckerman, and R. Brafman.  An MDP-based recommender systemJournal of Machine Learning Research 6: 1265-1295, 2005.  UAI 2002 version (local copy).

 

·         F. Bach, D. Heckerman, and E. Horvitz, On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers.  In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, Barbados, January 2005.  Also appears as Technical Report MSR-TR-04-124, Microsoft Research, November, 2004.

 

·         D. Chickering, D. Heckerman, and C. Meek, Large-Sample Learning of Bayesian Networks is NP-HardJournal of Machine Learning Research. 5: 1287-1330, 2004.

 

·         D. Heckerman, C. Meek, and T. Richardson, Variations on Undirected Graphical Models and their Relationships.  Technical Report MSR-TR-2004-95, Microsoft Research, September, 2004.

 

·         V. Jojic, N. Jojic, C. Meek, D. Geiger, A. Siepel, D. Haussler, and D. Heckerman: Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) 161-168, 2004.  Also appears as Technical Report MSR-TR-2003-62, Microsoft Research, October, 2003.

 

·         N. Jojic, V. Jojic, and D. Heckerman, Joint discovery of haplotype blocks and complex trait associations from SNP sequences.  In Proceedings of Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, UAI Press, July 2004.

 

·         B. Thiesson, D. Chickering, D. Heckerman, and C. Meek, ARMA Time-Series Modeling with Graphical Models.  In Proceedings of Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, UAI Press, July 2004.  Also appears as Technical Report MSR-TR-04-86, Microsoft Research, July, 2004.  Local copy.

 

·         D. Heckerman, C. Meek, and D. Koller, Probabilistic Models for Relational Data.  Technical Report MSR-TR-2004-30, Microsoft Research, March, 2004.

 

·         D. Chickering and D Heckerman. Targeted Advertising with Inventory Management.  Interfaces, 33:71-77, 2003.  Also appears as Technical Report MSR-TR-00-49, Microsoft Research, August, 2000.

 

·         I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White, Visualization of Navigation Patterns on a Web Site Using Model Based Clustering, Data Mining and Knowledge Discovery, 7:399-424, 2003.  Also appears as Technical Report MSR-TR-00-18, Microsoft Research, March, 2000.

 

·         G. Hulten, D.M. Chickering, D. Heckerman. Learning Bayesian Networks from Dependency Networks: A Preliminary Study. In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, January 2003.

 

·         C. Meek, B. Thiesson, and D. Heckerman,  Staged Mixture Modeling and Boosting.  In Proceedings of Eighteenth Conference on Uncertainty in Artificial Intelligence, Edmonton, Alberta, Morgan Kaufmann, August 2002.  Local copy.  Also appears as Technical Report MSR-TR-02-45, Microsoft Research, February, 2001.

 

·         C. Kadie, C. Meek, and D. Heckerman.  CFW: A collaborative filtering system using posteriors over weights of evidence.  In Proceedings of Eighteenth Conference on Uncertainty in Artificial Intelligence, Edmonton, Alberta, Morgan Kaufmann, August 2002.  Local copy.  Also appears as Technical Report MSR-TR-02-46, Microsoft Research, February, 2001.

 

·         C. Meek, D. Chickering and D. Heckerman.  Autoregressive tree models for time-series analysis. In Proceedings of the Second International SIAM Conference on Data Mining, Arlington, VA, SIAM, April, 2002.

 

·         D. Geiger and D. Heckerman. Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions.  The Annals of Statistics, 30: 1412-1440, 2002.  UAI 1999 version (local copy).  Also appears as Technical Report MSR-TR-98-67, Microsoft Research, December, 1998 (Revised January, 2002).

 

·         C. Meek, B. Thiesson, and D. Heckerman.  The Learning-Curve Sampling Method Applied to Model-Based Clustering.  Journal of Machine Learning Research, 2:397-418, 2001.  Also appears as Technical Report MSR-TR-01-34, Microsoft Research, February, 2001.

 

·         N. Jojic, P. Simard, B. J. Frey and D. Heckerman. Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching.  In Proceedings of Eighth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Morgan Kaufmann, January 2001.

 

·         B. Thiesson, C. Meek, and D. Heckerman. Accelerating EM for Large Databases Machine Learning, 45:279-299, 2001.  Also appears as Technical Report MSR-TR-99-31, Microsoft Research, May, 1999 (Revised February, 2001).

 

·         D. Chickering and D. Heckerman. A Decision-Theoretic Approach to Targeted Advertising.  In Proceedings of Sixteenth Conference on Uncertainty in Artificial Intelligence, Stanford, CA, Morgan Kaufmann, July 2000.  Also appears as Technical Report MSR-TR-00-17, Microsoft Research, February, 2000.

 

·         D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, C. Kadie. Dependency Networks for Density Estimation, Collaborative Filtering, and Data VisualizationJournal of Machine Learning Research. 1:49-75, 2000.  Also appears as Technical Report MSR-TR-00-16, Microsoft Research, February, 2000.

 

·         D. Heckerman and D. Chickering. A Comparison of Scientific and Engineering Criteria for Bayesian Model SelectionStatistics and Computing, 10:55-62, 2000.  Also appears as Technical Report MSR-TR-96-12, Microsoft Research, June, 1996 (revised November, 1996).

 

·         D. Chickering and D. Heckerman. Fast Learning from Sparse Data.  In Proceedings of Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, Morgan Kaufmann, August 1999.  Local copy.  Also appears as Technical Report MSR-TR-00-15, Microsoft Research, February, 1999 (Revised May, 1999).

 

·         D. Heckerman, C. Meek, and G. Cooper A Bayesian Approach to Causal Discovery.  In C. Glymour and G. Cooper, editors, Computation, Causation, and Discovery, pages 141-165.  MIT Press, Cambridge, MA, 1999.  Also appears as Technical Report MSR-TR-97-05, Microsoft Research, February, 1997.

 

·         D. Geiger, D. Heckerman, H. King, C. Meek. Stratified Exponential Families: Graphical Models and Model Selection.  The Annals of Statistics, 29:505-529, 2001.  Also appears as Technical Report MSR-TR-98-31, Microsoft Research, July, 1998.

 

·         S. T. Dumais, J. Platt, D. Heckerman and M. Sahami. Inductive Learning Algorithms and Representations for Text Categorization. (Word file). Proceedings of ACM-CIKM98, November, 1998.

 

·         M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian Approach to Filtering Junk E-mail. AAAI'98 Workshop on Learning for Text Categorization, July 27, 1998, Madison, Wisconsin.

 

·         D. Heckerman and E. Horvitz. Inferring Informational Goals from Free-Text Queries. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998.  Local copy.

 

·         E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998.

 

·         J. Breese, D. Heckerman, C. Kadie Empirical Analysis of Predictive Algorithms for Collaborative Filtering.  In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998.  Local copy.  Also appears as Technical Report MSR-TR-98-12, Microsoft Research, May, 1998 (revised October, 1998).

 

·         M. Meila and D. Heckerman An Experimental Comparison of Several Clustering and Initialization MethodsMachine Learning.  42:9-29, 2001.  UAI 1998 version (local copy).  Also appears as Technical Report MSR-TR-98-06, Microsoft Research, February, 1998.

 

·         B. Thiesson, C. Meek, D. Chickering, D. Heckerman.  Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models.  In J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics 6, pages 631-656, Oxford University Press, Oxford, 1999.  UAI 1998 version (local copy).  Also appears as Technical Report MSR-TR-97-30, Microsoft Research, December, 1997.

 

·         D. Heckerman and C. Meek. Models and Selection Criteria for Regression and Classification.  In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, RI, pages 223-228. Morgan Kaufmann, August 1997.  Also appears as Technical Report MSR-TR-97-08, Microsoft Research, May, 1997.

 

·         D. Chickering, D. Heckerman, C. Meek.  A Bayesian Approach to Learning Bayesian Networks with Local Structure.  In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, RI, pages 80-89. Morgan Kaufmann, August 1997.  Local copy.  Also appears as Technical Report MSR-TR-97-07, Microsoft Research, August, 1997.

 

·         C. Meek and D. Heckerman. Structure and parameter learning for causal independence and causal interaction models.  In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, RI, pages 366-375. Morgan Kaufmann, August 1997.  Local copy.

 

·         D. Heckerman and C. Meek Embedded Bayesian Network Classifiers Technical Report MSR-TR-97-06, Microsoft Research, March, 1997.

 

·         P. Smyth, D. Heckerman, M. Jordan. Probabilistic Independence Networks for Hidden Markov Probability ModelsNeural Computation, 9:227-269, 1997.  Also appears as Technical Report MSR-TR-96-03, Microsoft Research, January, 1996 (revised June, 1996).

 

·         D. Chickering and D. Heckerman. Efficient Approximations for the Marginal Likelihood of Bayesian Networks With Hidden VariablesMachine Learning, 29:181-212, 1997.  UAI 1996 version (local copy).  Also appears as Technical Report MSR-TR-96-08, Microsoft Research, March, 1996 (revised April, 1997).

 

·         D. Geiger, D. Heckerman, and C. Meek. Asymptotic Model Selection for Directed Networks with Hidden Variables.  In Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, Portland, OR, pages 283-290. Morgan Kaufmann, August 1996.  Local copy.  Also appears as Technical Report MSR-TR-96-07, Microsoft Research, May, 1996.

 

·         J. Breese and D. Heckerman. Decision-theoretic troubleshooting: A framework for repair and experiment.  In Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, Portland, OR, pages 124-132. Morgan Kaufmann, August 1996.  Also appears as Technical Report MSR-TR-96-06, Microsoft Research, March, 1996.  Local copy.

 

·         J. Breese and D. Heckerman.  Decision-theoretic case-based reasoningIEEE Transactions on Systems, Man, and Cybernetics, 26:838-842, 1996.  Local copy.  Also appears as MSR-TR-95-03, Microsoft Research, November, 1994 (revised August, 1995).

 

·         D. Geiger, D. Heckerman. A Characterization of the Dirichlet Distribution Through Global and Local Independence.  The Annals of Statistics, 25:1344-1369, 1997.  Also appears as Technical Report MSR-TR-94-16, Microsoft Research, November, 1994 (revised February, 1994).

 

·         D. Geiger and D. Heckerman.  Beyond Bayesian networks: Similarity networks and Bayesian multinets.  Artificial Intelligence, 82:45-74, 1996.

 

·         D. Heckerman, J. Breese. Causal Independence for Probability Assessment and Inference Using Bayesian Networks.  IEEE Transactions on Systems, Man, and Cybernetics, 26:826-831, 1996.  Local copy.  Also appears as Technical Report MSR-TR-94-08, Microsoft Research, March, 1994 (revised October, 1995).

 

·         D. Geiger, D. Heckerman. A Characterization of the Bivariate Normal-Wishart Distribution. Technical Report MSR-TR-95-53, Microsoft Research, November, 1995.

 

·         D. Heckerman, R. Shachter. Decision-Theoretic Foundations for Causal Reasoning.  Journal of Artificial Intelligence Research, 3:405-430, 1995.  Also appears a Technical Report MSR-TR-94-11, Microsoft Research, March, 1994 (revised December, 1995).

 

·         D. Heckerman, D. Geiger, D. Chickering. Learning Bayesian networks: The Combination of Knowledge and Statistical Data.  Machine Learning, 20:197-243, 1995.  Also appears as Technical Report MSR-TR-94-09, Microsoft Research, March, 1994 (revised December, 1994).

 

·         D. Chickering, D. Geiger, and D. Heckerman.  On finding a cycle basis with a shortest maximal cycleInformation Processing Letters, 54:55-58, 1995.

 

·         D. Heckerman. A Bayesian approach to learning causal networks.  In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 285-295. Morgan Kaufmann, August 1995.  Local copy.  Also appears as Technical Report MSR-TR-95-04, Microsoft Research, March, 1995.

 

·         D. Geiger and D. Heckerman. A characterization of the Dirichlet distribution with applications to learning Bayesian networks.  In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 196-207. Morgan Kaufmann, August 1995.  Local copy.

 

·         D. Heckerman and R. Shachter. A definition and graphical representation for causality.  In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 262-273. Morgan Kaufmann, August 1995.  Local copy.

 

·         D. Heckerman and D. Geiger. Learning Bayesian networks: A unification for discrete and Gaussian domains.  In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, pages 274-284. Morgan Kaufmann, August 1995.  Local copy.

 

·         J. Breese and D. Heckerman.  Decision-theoretic case-based reasoning.  In Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL. Society for Artificial Intelligence in Statistics, January 1995.

 

·         D. Heckerman and R. Shachter.  A decision-based view of causality.  In Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL. Society for Artificial Intelligence in Statistics, January 1995.

 

·         D. Heckerman, J. Breese, K. Rommelse.  Decision-theoretic troubleshootingCACM, 38:49-57, 1995.

 

·         D. Heckerman, J. Breese, and K. Rommelse.  Troubleshooting under uncertainty.  In Proceedings of Fifth International Workshop on Principles of Diagnosis, New Paltz, NY, pages 121-130, October 1994.  Also appears as Technical Report MSR-TR-94-07, Microsoft Research, March, 1994.

 

·         D. Heckerman, D. Geiger, and D. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data.  In Proceedings of Tenth Conference on Uncertainty in Artificial  Intelligence, Seattle, WA, pages 293-301. Morgan Kaufmann, July 1994.  Local copy.

 

·         D. Geiger and D. Heckerman. Learning Gaussian networks.  In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 235-243. Morgan Kaufmann, July 1994.

 

·         D. Heckerman and J. Breese.  A new look at causal independence.  In Proceedings of Tenth Conference on Uncertainty in Artificial  Intelligence, Seattle, WA, pages 286-292. Morgan Kaufmann, July 1994.  Local copy.

 

·         D. Heckerman and R. Shachter.  A decision-based view of causality.  In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 302-310. Morgan Kaufmann, July 1994.  Local copy.

 

·         D. Geiger and D. Heckerman.  Inference algorithms for similarity networks.  In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 326-334. Morgan Kaufmann, July 1993.  Local copy.

 

·         D. Heckerman.  Causal independence for knowledge acquisition and inference.  In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 122-127. Morgan Kaufmann, July 1993.  Local copy.

 

·         D. Heckerman and M. Shwe.  Diagnosis of multiple faults: A sensitivity analysis.  In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 80-87. Morgan Kaufmann, July 1993.  Local copy.

 

·         D. Heckerman and E. Horvitz.  Problem formulation as the reduction of a decision model.  In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 80-87. Morgan Kaufmann, July 1993.  Local copy.

 

·         D. Heckerman, E. Horvitz, and B. Middleton.  An approximate nonmyopic computation for value of informationIEEE Transactions on Pattern Analysis and Machine Intelligence, 15:292-298, 1993.

 

·         D. Heckerman, E. Horvitz, and B. Nathwani.  Toward normative expert systems: Part I.  The Pathfinder project.  Methods of Information in Medicine, 31:90-105, 1992.  Local copy.

 

·         D. Heckerman and B. Nathwani.  Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inferenceMethods of Information in Medicine, 31:106-116, 1992.  Also in van Bemmel, J., McCray, A., editors, Yearbook of Medical Informatics, pages 430-440. International Medical Informatics Association, Rotterdam, The Netherlands, 1993.  Local copy.

 

·         D. Geiger and D. Heckerman.  Dependence and Relevance: A probabilistic view.  arXiv:1611.02126, February 1993.

 

·         D. Heckerman.  The certainty-factor model.  In S. Shapiro, editor, Encyclopedia of Artificial Intelligence, Second Edition, pages 131-138.  Wiley, New York, 1992.

 

·         D. Heckerman and E. Shortliffe.  From certainty factors to belief networksArtificial Intelligence in Medicine, 4:35-52, 1992.  Local copy.

 

·         D. Heckerman and B. Nathwani.  An evaluation of the diagnostic accuracy of PathfinderComputers and Biomedical Research, 25:56-74, 1992.  Local copy.

 

·         M. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper.  Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base:  Part 1. The probabilistic model and inference algorithmsMethods of Information in Medicine, 30:241-255, 1991.

 

·         B. Middleton, M. Shwe, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper.  Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base:  Part 2.  Evaluation of diagnostic performanceMethods of Information in Medicine, 30:256-267, 1991.

 

·         D. Heckerman, E. Horvitz, and B. Middleton.  An approximate nonmyopic computation for value of information.  In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 135-141. Morgan Kaufmann, July 1991.  Local copy.

 

·         D. Geiger and D. Heckerman.  Advances in probabilistic reasoning.  In Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 118-126. Morgan Kaufmann, July 1991.  Local copy.

 

·         D. Heckerman.  Probabilistic Similarity Networks.  MIT Press, Cambridge, MA, 1991.  Review.

 

·         D. Heckerman.  Probabilistic similarity networks.  Networks, 20:607-636, 1990.

 

·         B. Nathwani, D. Heckerman, E. Horvitz, and T. Lincoln.  Integrated expert systems and videodisc in surgical pathology: An overview. Human Pathology, 21:11-27, 1990.

 

·         D. Heckerman.  Similarity networks for the construction of multiple-fault belief  networks.  In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 32-39. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.  Also in P. Bonissone, M. Henrion, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 6, pages 51-64.  North-Holland, New York, 1990.  Local copy.

 

·         D. Heckerman and E. Horvitz.  Problem formulation as the reduction of a decision model.  In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 82-89. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.  Also in P. Bonissone, M. Henrion, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 6, pages 159-170.  North-Holland, New York, 1990.

 

·         D. Geiger and D. Heckerman.  Separable and transitive graphoids.  In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 538-545. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.  Local copy.

 

·         H. Suermondt, G. Cooper, and D. Heckerman.  A combination of cutset conditioning with clique-tree propagation in the Pathfinder system.  In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 273-279. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1990.  Local copy.

 

·         D. Heckerman.  A tractable algorithm for diagnosing multiple diseases.  In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, pages 174-181. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1989.  Also in M. Henrion, R. Shachter, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 5, pages 163-171.  North-Holland, New York, 1990.  Local copy.

 

·         D. Heckerman, J. Breese, and E. Horvitz.  The compilation of decision models.  In Proceedings of Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, ON, pages 162-173. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1989.  Local copy.

 

·         D. Heckerman, E. Horvitz, and B. Nathwani.  Update on the Pathfinder project.  In Proceedings of the Thirteenth Symposium on Computer Applications in Medical Care, Washington, D, pages 203-207.  IEEE Computer Society Press, Silver Spring, MD, November 1989.  Local copy.

 

·         E. Horvitz, G. Cooper, and D. Heckerman.  Reflection and action under scarce resources: Theoretical principles and empirical study.  In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI, pages 1121-1127. Morgan Kaufmann, San Mateo, CA, August 1989.  Local copy.

 

·         E. Horvitz, D. Heckerman, K. Ng, and B. Nathwani.  Heuristic abstraction in the decision-theoretic Pathfinder system.  In Proceedings of the Thirteenth Symposium on Computer Applications in Medical Care, Washington, DC, pages 178-182. IEEE Computer Society Press, Silver Spring, MD, November 1989.

 

·         D. Heckerman.  An empirical comparison of three inference methods.  In Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, Minneapolis, MN, pages 158-169. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1988.  Also in R. Shachter, T. Levitt, L. Kanal, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 4, pages 283-302.  North-Holland, New York, 1990.  Local copy.

 

·         D. Heckerman and E. Horvitz.  On the expressiveness of rule-based systems for reasoning under uncertainty.  In Proceedings AAAI-87 Sixth National Conference on Artificial Intelligence, Seattle, WA, pages 121-126. Morgan Kaufmann, San Mateo, CA, July 1987.  Local copy.

 

·         D. Heckerman and H. Jimison.  A perspective on confidence and its use in focusing attention during knowledge acquisition.  In Proceedings of the Third Workshop on Uncertainty in Artificial Intelligence, Seattle, WA, pages 123-131. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, July 1987.  Also in L. Kanal, T. Levitt, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 3, pages 123-131. North-Holland, New York, 1989.  Local copy.

 

·         R. Shachter and D. Heckerman.  Thinking backward for knowledge acquisitionAI Magazine, 8:55-63, 1987.  Local copy.

 

·         D. Heckerman.  An axiomatic framework for belief updates.  In Proceedings of the Second Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, pages 123-128. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1986.  Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence 2, pages 11-22. North-Holland, New York, 1988.  Local copy.

 

·         D. Heckerman and E. Horvitz.  The myth of modularity in rule-based systems.  In Proceedings of the Second Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, pages 115-121. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1986.  Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence 2, pages 23-34. North-Holland, New York, 1988.

 

·         D. Heckerman and R. Miller.  Towards a better understanding of the INTERNIST-1 knowledge base.  In Proceedings of Medinfo, Washington, DC, pages 27-31.  North-Holland, New York, October 1986.

 

·         E. Horvitz, D. Heckerman, and C. Langlotz.  A framework for comparing alternative formalisms for plausible reasoning.  In Proceedings AAAI-86 Fifth National Conference on Artificial Intelligence, Philadelphia, PA, pages 210-214. Morgan Kaufmann, San Mateo, CA, August 1986.  Local copy.

 

·         E. Horvitz, D. Heckerman, B. Nathwani, and L. Fagan.  The use of a heuristic problem-solving hierarchy to facilitate the explanation of hypothesis-directed reasoning.  In Proceedings of Medinfo, Washington, DC, pages 27-31.  North-Holland, New York, October 1986.

 

·         E. Horvitz and D. Heckerman.  The inconsistent use of measures of certainty in artificial intelligence research.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985.  Also in L. Kanal and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 137-151. North-Holland, New York, 1986.

 

·         D. Heckerman.  Probabilistic interpretations for MYCIN's certainty factors.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA, pages 9-20. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985.  Also in L. Kanal. and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167-196. North-Holland, New York, 1986.