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ORCIDDavid Heckerman
David E. Heckerman – David Earl Heckerman
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- affiliation: Amazon
- affiliation (former): Microsoft Research
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2020 – today
- 2024
[c77]Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, David Heckerman:
Multiply-Robust Causal Change Attribution. ICML 2024- [i65]Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman:
Multiply-Robust Causal Change Attribution. CoRR abs/2404.08839 (2024) - [i64]Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan, Payman Arabshahi, David Heckerman:
Fast Training Dataset Attribution via In-Context Learning. CoRR abs/2408.11852 (2024) - [i63]Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan, Payman Arabshahi, David Heckerman:
Removing Spurious Correlation from Neural Network Interpretations. CoRR abs/2412.02893 (2024) - 2023
- [i62]David Heckerman:
Heckerthoughts. CoRR abs/2302.05449 (2023) - 2022
- [c76]Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman:
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. ICML 2022: 1313-1326 - [p7]Ross D. Shachter, David Heckerman:
Why Did They Do That? Probabilistic and Causal Inference 2022: 805-812 - 2021
- [c75]Mohammad Taha Bahadori, David Heckerman:
Debiasing Concept-based Explanations with Causal Analysis. ICLR 2021 - [i61]Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions. CoRR abs/2105.03248 (2021) - [i60]David Heckerman, Dan Geiger:
Likelihoods and Parameter Priors for Bayesian Networks. CoRR abs/2105.06241 (2021) - [i59]Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David E. Heckerman:
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. CoRR abs/2107.13068 (2021) - 2020
- [i58]David Heckerman:
A Tutorial on Learning With Bayesian Networks. CoRR abs/2002.00269 (2020) - [i57]Mohammad Taha Bahadori, David E. Heckerman:
Debiasing Concept Bottleneck Models with Instrumental Variables. CoRR abs/2007.11500 (2020)
2010 – 2019
- 2019
- [j48]David Heckerman
:
Toward Accounting for Hidden Common Causes When Inferring Cause and Effect from Observational Data. ACM Trans. Intell. Syst. Technol. 10(5): 51:1-51:5 (2019) - [c74]David Heckerman:
Exploiting High Dimensionality in Big Data. KDD 2019: 3172 - [i56]David Heckerman, Christopher Meek:
Embedded Bayesian Network Classifiers. CoRR abs/1910.09715 (2019) - [i55]David Heckerman:
Probabilistic Similarity Networks. CoRR abs/1911.06263 (2019) - 2018
- [j47]Seunghak Lee, Nico Görnitz, Eric P. Xing, David Heckerman, Christoph Lippert:
Ensembles of Lasso Screening Rules. IEEE Trans. Pattern Anal. Mach. Intell. 40(12): 2841-2852 (2018) - [i54]David Heckerman:
Accounting for hidden common causes when inferring cause and effect from observational data. CoRR abs/1801.00727 (2018) - 2016
- [i53]Dan Geiger, David Heckerman:
Dependence and Relevance: A probabilistic view. CoRR abs/1611.02126 (2016) - 2015
- [j46]Christoph Lippert, David Heckerman:
Computational and statistical issues in personalized medicine. XRDS 21(4): 24-27 (2015) - [j45]Gabriel Rodrigues Alves Margarido, David Heckerman:
ConPADE: Genome Assembly Ploidy Estimation from Next-Generation Sequencing Data. PLoS Comput. Biol. 11(4) (2015) - 2014
- [j44]Hoifung Poon, Chris Quirk, Charlie DeZiel, David Heckerman:
Literome: PubMed-scale genomic knowledge base in the cloud. Bioinform. 30(19): 2840-2842 (2014) - [j43]Christoph Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl Myers Kadie, David Heckerman, Jennifer Listgarten:
Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinform. 30(22): 3206-3214 (2014) - [j42]David Heckerman, Christopher Meek, Thomas S. Richardson:
Variations on undirected graphical models and their relationships. Kybernetika 50(3): 363-377 (2014) - [i52]Eric Horvitz, David Heckerman:
Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research. CoRR abs/1407.7281 (2014) - 2013
- [j41]Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl Myers Kadie, David Heckerman:
A powerful and efficient set test for genetic markers that handles confounders. Bioinform. 29(12): 1526-1533 (2013) - [i51]Carl Myers Kadie, Christopher Meek, David Heckerman:
CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence. CoRR abs/1301.0575 (2013) - [i50]Christopher Meek, Bo Thiesson, David Heckerman:
Staged Mixture Modelling and Boosting. CoRR abs/1301.0586 (2013) - [i49]Guy Shani, Ronen I. Brafman, David Heckerman:
An MDP-based Recommender System. CoRR abs/1301.0600 (2013) - [i48]David Maxwell Chickering, David Heckerman:
A Decision Theoretic Approach to Targeted Advertising. CoRR abs/1301.3842 (2013) - [i47]David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie:
Dependency Networks for Collaborative Filtering and Data Visualization. CoRR abs/1301.3862 (2013) - [i46]David Maxwell Chickering, David Heckerman:
Fast Learning from Sparse Data. CoRR abs/1301.6685 (2013) - [i45]Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions. CoRR abs/1301.6697 (2013) - [i44]John S. Breese, David Heckerman, Carl Myers Kadie:
Empirical Analysis of Predictive Algorithms for Collaborative Filtering. CoRR abs/1301.7363 (2013) - [i43]David Heckerman, Eric Horvitz:
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. CoRR abs/1301.7382 (2013) - [i42]Eric Horvitz, John S. Breese, David Heckerman, David Hovel, Koos Rommelse:
The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. CoRR abs/1301.7385 (2013) - [i41]Marina Meila, David Heckerman:
An Experimental Comparison of Several Clustering and Initialization Methods. CoRR abs/1301.7401 (2013) - [i40]Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman:
Learning Mixtures of DAG Models. CoRR abs/1301.7415 (2013) - [i39]David Maxwell Chickering, David Heckerman, Christopher Meek:
A Bayesian Approach to Learning Bayesian Networks with Local Structure. CoRR abs/1302.1528 (2013) - [i38]David Heckerman, Christopher Meek:
Models and Selection Criteria for Regression and Classification. CoRR abs/1302.1545 (2013) - [i37]Christopher Meek, David Heckerman:
Structure and Parameter Learning for Causal Independence and Causal Interaction Models. CoRR abs/1302.1561 (2013) - [i36]John S. Breese, David Heckerman:
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment. CoRR abs/1302.3563 (2013) - [i35]David Maxwell Chickering, David Heckerman:
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. CoRR abs/1302.3567 (2013) - [i34]Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. CoRR abs/1302.3580 (2013) - [i33]Dan Geiger, David Heckerman:
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. CoRR abs/1302.4949 (2013) - [i32]David Heckerman, Ross D. Shachter:
A Definition and Graphical Representation for Causality. CoRR abs/1302.4956 (2013) - [i31]David Heckerman, Dan Geiger:
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. CoRR abs/1302.4957 (2013) - [i30]David Heckerman:
A Bayesian Approach to Learning Causal Networks. CoRR abs/1302.4958 (2013) - [i29]Dan Geiger, David Heckerman:
Learning Gaussian Networks. CoRR abs/1302.6808 (2013) - [i28]David Heckerman, John S. Breese:
A New Look at Causal Independence. CoRR abs/1302.6814 (2013) - [i27]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. CoRR abs/1302.6815 (2013) - [i26]David Heckerman, Ross D. Shachter:
A Decision-Based View of Causality. CoRR abs/1302.6816 (2013) - [i25]David Heckerman, Michael Shwe:
Diagnosis of Multiple Faults: A Sensitivity Analysis. CoRR abs/1303.1463 (2013) - [i24]David Heckerman:
Causal Independence for Knowledge Acquisition and Inference. CoRR abs/1303.1468 (2013) - [i23]Dan Geiger, David Heckerman:
Inference Algorithms for Similarity Networks. CoRR abs/1303.1493 (2013) - [i22]Dan Geiger, David Heckerman:
Advances in Probabilistic Reasoning. CoRR abs/1303.5718 (2013) - [i21]David Heckerman, Eric Horvitz, Blackford Middleton:
An Approximate Nonmyopic Computation for Value of Information. CoRR abs/1303.5720 (2013) - [i20]David Heckerman:
Similarity Networks for the Construction of Multiple-Faults Belief Networks. CoRR abs/1304.1085 (2013) - [i19]David Heckerman, Eric Horvitz:
Problem Formulation as the Reduction of a Decision Model. CoRR abs/1304.1091 (2013) - [i18]Henri Jacques Suermondt, Gregory F. Cooper, David Heckerman:
A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System. CoRR abs/1304.1114 (2013) - [i17]Dan Geiger, David Heckerman:
Practical and Theoretical Advances in Knowledge Acquisition of Probabilistic Networks. CoRR abs/1304.1145 (2013) - [i16]David Heckerman, John S. Breese, Eric Horvitz:
The Compilation of Decision Models. CoRR abs/1304.1510 (2013) - [i15]David Heckerman:
A Tractable Inference Algorithm for Diagnosing Multiple Diseases. CoRR abs/1304.1511 (2013) - [i14]David Heckerman:
An Empirical Comparison of Three Inference Methods. CoRR abs/1304.2357 (2013) - [i13]David Heckerman, Holly Brügge Jimison:
A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition. CoRR abs/1304.2724 (2013) - [i12]Michael P. Wellman, David Heckerman:
The Role of Calculi in Uncertain Inference Systems. CoRR abs/1304.2747 (2013) - [i11]David Heckerman, Eric Horvitz:
The Myth of Modularity in Rule-Based Systems. CoRR abs/1304.3090 (2013) - [i10]David Heckerman:
An Axiomatic Framework for Belief Updates. CoRR abs/1304.3091 (2013) - [i9]Ross D. Shachter, David Heckerman:
A Backwards View for Assessment. CoRR abs/1304.3107 (2013) - [i8]David Heckerman:
Probabilistic Interpretations for MYCIN's Certainty Factors. CoRR abs/1304.3419 (2013) - [i7]David Heckerman, E. H. Mamdani:
Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993). CoRR abs/1304.3851 (2013) - 2012
- [c73]Wei Cheng, Xiang Zhang, Yubao Wu, Xiaolin Yin, Jing Li, David Heckerman, Wei Wang:
Inferring novel associations between SNP sets and gene sets in eQTL study using sparse graphical model. BCB 2012: 466-473 - [i6]Chong Wang, David M. Blei, David Heckerman:
Continuous Time Dynamic Topic Models. CoRR abs/1206.3298 (2012) - [i5]Jennifer Listgarten, David Heckerman:
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach. CoRR abs/1206.5269 (2012) - [i4]Nebojsa Jojic, Vladimir Jojic, David Heckerman:
Joint discovery of haplotype blocks and complex trait associations from SNP sequences. CoRR abs/1207.4145 (2012) - [i3]Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek:
ARMA Time-Series Modeling with Graphical Models. CoRR abs/1207.4162 (2012) - [i2]David Maxwell Chickering, Christopher Meek, David Heckerman:
Large-Sample Learning of Bayesian Networks is NP-Hard. CoRR abs/1212.2468 (2012) - 2011
- [c72]Jennifer Listgarten, Carl Myers Kadie, Eric E. Schadt, David Heckerman:
Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract). UAI 2011: 852
2000 – 2009
- 2009
- [j40]Bongshin Lee, Lev Nachmanson, George G. Robertson, Jonathan M. Carlson, David Heckerman:
PhyloDet: a scalable visualization tool for mapping multiple traits to large evolutionary trees. Bioinform. 25(19): 2611-2612 (2009) - [p6]Joel Robertson, Del DeHart, Kristin M. Tolle, David Heckerman:
Healthcare delivery in developing countries: challenges and potential solutions. The Fourth Paradigm 2009: 65-73 - 2008
- [j39]Noah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic:
Shift-Invariant Adaptive Double Threading: Learning MHC II-Peptide Binding. J. Comput. Biol. 15(7): 927-942 (2008) - [j38]David C. Nickle, Nebojsa Jojic, David Heckerman, Vladimir Jojic, Darko Kirovski, Morgane Rolland, Sergei L. Kosakovsky Pond, James I. Mullins:
Comparison of Immunogen Designs That Optimize Peptide Coverage: Reply to Fischer et al. PLoS Comput. Biol. 4(1) (2008) - [j37]Jennifer Listgarten, Zabrina L. Brumme, Carl Myers Kadie, Gao Xiaojiang, Bruce D. Walker, Mary Carrington, Philip J. R. Goulder, David Heckerman:
Statistical Resolution of Ambiguous HLA Typing Data. PLoS Comput. Biol. 4(2) (2008) - [j36]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:
Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 Gag. PLoS Comput. Biol. 4(11) (2008) - [c71]Chong Wang, David M. Blei, David Heckerman:
Continuous Time Dynamic Topic Models. UAI 2008: 579-586 - [p5]David Heckerman:
A Tutorial on Learning with Bayesian Networks. Innovations in Bayesian Networks 2008: 33-82 - 2007
- [j35]Joshua Goodman, Gordon V. Cormack, David Heckerman:
Spam and the ongoing battle for the inbox. Commun. ACM 50(2): 24-33 (2007) - [j34]David Heckerman, Carl Myers Kadie, Jennifer Listgarten:
Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. J. Comput. Biol. 14(6): 736-746 (2007) - [j33]David C. Nickle, Morgane Rolland, Mark A. Jensen, Sergei L. Kosakovsky Pond, Wenjie Deng, Mark Seligman, David Heckerman, James I. Mullins, Nebojsa Jojic:
Coping with Viral Diversity in HIV Vaccine Design. PLoS Comput. Biol. 3(4) (2007) - [j32]Jennifer Listgarten, Nicole Frahm, Carl Myers Kadie, Christian Brander, David Heckerman:
A Statistical Framework for Modeling HLA-Dependent T Cell Response Data. PLoS Comput. Biol. 3(10) (2007) - [c70]Noah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic:
Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide Binding. RECOMB 2007: 181-195 - [c69]Jennifer Listgarten, David Heckerman:
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach. UAI 2007: 251-258 - 2006
- [j31]Francis R. Bach, David Heckerman, Eric Horvitz:
Considering Cost Asymmetry in Learning Classifiers. J. Mach. Learn. Res. 7: 1713-1741 (2006) - [c68]Nebojsa Jojic, Manuel Reyes-Gomez, David Heckerman, Carl Myers Kadie, Ora Schueler-Furman:
Learning MHC I - peptide binding. ISMB (Supplement of Bioinformatics) 2006: 227-235 - [c67]David Heckerman, Carl Myers Kadie, Jennifer Listgarten:
Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. RECOMB 2006: 296-308 - 2005
- [j30]David Heckerman, Tom Berson, Joshua Goodman, Andrew Y. Ng:
The First Conference on E-mail and Anti-Spam. AI Mag. 26(1): 96 (2005) - [j29]Guy Shani, David Heckerman, Ronen I. Brafman:
An MDP-Based Recommender System. J. Mach. Learn. Res. 6: 1265-1295 (2005) - [c66]Francis R. Bach, David Heckerman, Eric Horvitz:
On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers. AISTATS 2005: 9-16 - [c65]Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman:
Using epitomes to model genetic diversity: Rational design of HIV vaccines. NIPS 2005: 587-594 - 2004
- [j28]David Maxwell Chickering, David Heckerman, Christopher Meek:
Large-Sample Learning of Bayesian Networks is NP-Hard. J. Mach. Learn. Res. 5: 1287-1330 (2004) - [c64]Vladimir Jojic, Nebojsa Jojic, Christopher Meek, Dan Geiger, Adam C. Siepel, David Haussler, David Heckerman:
Efficient approximations for learning phylogenetic HMM models from data. ISMB/ECCB (Supplement of Bioinformatics) 2004: 161-168 - [c63]David Heckerman:
Graphical models for data mining. KDD 2004: 2 - [c62]Nebojsa Jojic, Vladimir Jojic, David Heckerman:
Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences. UAI 2004: 286-292 - [c61]Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek:
ARMA Time-Series Modeling with Graphical Models. UAI 2004: 552-560 - 2003
- [j27]Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White:
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Min. Knowl. Discov. 7(4): 399-424 (2003) - [j26]David Maxwell Chickering, David Heckerman:
Targeted Advertising on the Web with Inventory Management. Interfaces 33(5): 71-77 (2003) - [c60]Ronen I. Brafman, David Heckerman, Guy Shani:
Recommendation as a Stochastic Sequential Decision Problem. ICAPS 2003: 164-173 - [c59]Geoff Hulten, David Maxwell Chickering, David Heckerman:
Learning Bayesian Networks From Dependency Networks: A Preliminary Study. AISTATS 2003: 141-148 - [c58]David Maxwell Chickering, Christopher Meek, David Heckerman:
Large-Sample Learning of Bayesian Networks is NP-Hard. UAI 2003: 124-133 - 2002
- [j25]Christopher Meek, Bo Thiesson, David Heckerman:
The Learning-Curve Sampling Method Applied to Model-Based Clustering. J. Mach. Learn. Res. 2: 397-418 (2002) - [c57]Christopher Meek, David Maxwell Chickering, David Heckerman:
Autoregressive Tree Models for Time-Series Analysis. SDM 2002: 229-244 - [c56]Carl Myers Kadie, Christopher Meek, David Heckerman:
CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence. UAI 2002: 242-250 - [c55]Christopher Meek, Bo Thiesson, David Heckerman:
Staged Mixture Modelling and Boosting. UAI 2002: 335-343 - [c54]Guy Shani, Ronen I. Brafman, David Heckerman:
An MDP-based Recommender System. UAI 2002: 453-460 - 2001
- [j24]Paolo Giudici, David Heckerman, Joe Whittaker:
Statistical Models for Data Mining. Data Min. Knowl. Discov. 5(3): 163-165 (2001) - [j23]Marina Meila, David Heckerman:
An Experimental Comparison of Model-Based Clustering Methods. Mach. Learn. 42(1/2): 9-29 (2001) - [j22]Bo Thiesson, Christopher Meek, David Heckerman:
Accelerating EM for Large Databases. Mach. Learn. 45(3): 279-299 (2001) - [c53]Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman:
Learning mixtures of smooth, nonuniform deformation models for probabilistic image matching. AISTATS 2001: 137-142 - [c52]Christopher Meek, Bo Thiesson, David Heckerman:
The Learning Curve Method Applied to Clustering. AISTATS 2001: 196-202 - [c51]Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman:
Separating Appearance from Deformation. ICCV 2001: 288-294 - 2000
- [j21]David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie:
Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. J. Mach. Learn. Res. 1: 49-75 (2000) - [j20]David Maxwell Chickering, David Heckerman:
A comparison of scientific and engineering criteria for Bayesian model selection. Stat. Comput. 10(1): 55-62 (2000) - [c50]Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White:
Visualization of navigation patterns on a Web site using model-based clustering. KDD 2000: 280-284 - [c49]David Maxwell Chickering, David Heckerman:
Targeted advertising with inventory management. EC 2000: 145-149 - [c48]David Maxwell Chickering, David Heckerman:
A Decision Theoretic Approach to Targeted Advertising. UAI 2000: 82-88 - [c47]David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie:
Dependency Networks for Collaborative Filtering and Data Visualization. UAI 2000: 264-273
1990 – 1999
- 1999
- [c46]Dan Geiger, David Heckerman, Henry King, Christopher Meek:
On the geometry of DAG models with hidden variables. AISTATS 1999 - [c45]David Maxwell Chickering, David Heckerman:
Fast Learning from Sparse Data. UAI 1999: 109-115 - [c44]Dan Geiger, David Heckerman:
Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions. UAI 1999: 216-225 - [e3]David Heckerman, Joe Whittaker:
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, AISTATS 1999, Fort Lauderdale, Florida, USA, January 3-6, 1999. Society for Artificial Intelligence and Statistics 1999 [contents] - 1998
- [j19]Dan Geiger, David Heckerman:
Probabilistic relevance relations. IEEE Trans. Syst. Man Cybern. Part A 28(1): 17-25 (1998) - [c43]John S. Breese, David Heckerman, Carl Myers Kadie:
Empirical Analysis of Predictive Algorithms for Collaborative Filtering. UAI 1998: 43-52 - [c42]David Heckerman, Eric Horvitz:
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. UAI 1998: 230-237 - [c41]Eric Horvitz, Jack S. Breese, David Heckerman, David Hovel, Koos Rommelse:
The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. UAI 1998: 256-265 - [c40]Marina Meila, David Heckerman:
An Experimental Comparison of Several Clustering and Initialization Methods. UAI 1998: 386-395 - [c39]Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman:
Learning Mixtures of DAG Models. UAI 1998: 504-513 - [p4]David Heckerman:
A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models 1998: 301-354 - [p3]Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. Learning in Graphical Models 1998: 461-477 - [d1]Jack S. Breese, David Heckerman, Carl Myers Kadie:
Anonymous Microsoft Web Data. UCI Machine Learning Repository, 1998
- 1997
- [j18]David Heckerman:
Bayesian Networks for Data Mining. Data Min. Knowl. Discov. 1(1): 79-119 (1997) - [j17]David Maxwell Chickering, David Heckerman:
Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables. Mach. Learn. 29(2-3): 181-212 (1997) - [j16]Padhraic Smyth, David Heckerman, Michael I. Jordan:
Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Comput. 9(2): 227-269 (1997) - [c38]David Heckerman, David Maxwell Chickering:
A Comparison of Scientific and Engineering Criteria for Bayesian Model Selection. AISTATS 1997: 275-282 - [c37]Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart Russell:
Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) 1997: 10-15 - [c36]David Maxwell Chickering, David Heckerman, Christopher Meek:
A Bayesian Approach to Learning Bayesian Networks with Local Structure. UAI 1997: 80-89 - [c35]David Heckerman, Christopher Meek:
Models and Selection Criteria for Regression and Classification. UAI 1997: 223-228 - [c34]Christopher Meek, David Heckerman:
Structure and Parameter Learning for Causal Independence and Causal Interaction Models. UAI 1997: 366-375 - [e2]David Heckerman, Heikki Mannila, Daryl Pregibon:
Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, August 14-17, 1997. AAAI Press 1997, ISBN 1-57735-027-8 [contents] - 1996
- [j15]Dan Geiger, David Heckerman:
Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets. Artif. Intell. 82(1-2): 45-74 (1996) - [j14]David Heckerman, John S. Breese:
Causal independence for probability assessment and inference using Bayesian networks. IEEE Trans. Syst. Man Cybern. Part A 26(6): 826-831 (1996) - [j13]John S. Breese, David Heckerman:
Decision-theoretic case-based reasoning. IEEE Trans. Syst. Man Cybern. Part A 26(6): 838-842 (1996) - [c33]John S. Breese, David Heckerman:
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment. UAI 1996: 124-132 - [c32]David Maxwell Chickering, David Heckerman:
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. UAI 1996: 158-168 - [c31]Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. UAI 1996: 283-290 - [p2]Max Henrion, Henri Jacques Suermondt, David Heckerman:
Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic Introduction. Uncertainty Management in Information Systems 1996: 255-284 - [p1]David Heckerman:
Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery and Data Mining 1996: 273-305 - 1995
- [j12]David Heckerman, E. H. Mamdani, Michael P. Wellman:
Real-World Applications of Bayesian Networks - Introduction. Commun. ACM 38(3): 24-26 (1995) - [j11]David Heckerman, Michael P. Wellman:
Bayesian Networks. Commun. ACM 38(3): 27-30 (1995) - [j10]David Heckerman, John S. Breese, Koos Rommelse:
Decision-Theoretic Troubleshooting. Commun. ACM 38(3): 49-57 (1995) - [j9]David Heckerman, E. H. Mamdani, Michael P. Wellman:
Editorial: real-world applications of uncertain reasoning. Int. J. Hum. Comput. Stud. 42(6): 573-574 (1995) - [j8]David Maxwell Chickering, Dan Geiger, David Heckerman:
On Finding a Cycle Basis with a Shortest Maximal Cycle. Inf. Process. Lett. 54(1): 55-58 (1995) - [j7]David Heckerman, Ross D. Shachter:
Decision-Theoretic Foundations for Causal Reasoning. J. Artif. Intell. Res. 3: 405-430 (1995) - [j6]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Mach. Learn. 20(3): 197-243 (1995) - [c30]David Heckerman:
Learning With Bayesian Networks (Abstract). ICML 1995: 588 - [c29]Dan Geiger, David Heckerman:
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI 1995: 196-207 - [c28]David Heckerman, Ross D. Shachter:
A Definition and Graphical Representation for Causality. UAI 1995: 262-273 - [c27]David Heckerman, Dan Geiger:
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI 1995: 274-284 - [c26]David Heckerman:
A Bayesian Approach to Learning Causal Networks. UAI 1995: 285-295 - [i1]David Heckerman, Ross D. Shachter:
Decision-Theoretic Foundations for Causal Reasoning. CoRR abs/cs/9512104 (1995) - 1994
- [c25]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. KDD Workshop 1994: 85-96 - [c24]Dan Geiger, David Heckerman:
Learning Gaussian Networks. UAI 1994: 235-243 - [c23]David Heckerman, John S. Breese:
A New Look at Causal Independence. UAI 1994: 286-292 - [c22]David Heckerman, Dan Geiger, David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI 1994: 293-301 - [c21]David Heckerman, Ross D. Shachter:
A Decision-based View of Causality. UAI 1994: 302-310 - 1993
- [j5]David Heckerman, Eric Horvitz, Blackford Middleton:
An Approximate Nonmyopic Computation for Value of Information. IEEE Trans. Pattern Anal. Mach. Intell. 15(3): 292-298 (1993) - [c20]David Heckerman, Michael Shwe:
Diagnosis of Multiple Faults: A Sensitivity Analysis. UAI 1993: 80-90 - [c19]David Heckerman:
Causal Independence for Knowledge Acquisition and Inference. UAI 1993: 122-127 - [c18]Dan Geiger, David Heckerman:
Inference Algorithms for Similarity Networks. UAI 1993: 326-334 - [e1]David Heckerman, E. H. Mamdani:
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. Morgan Kaufmann 1993, ISBN 1-55860-306-9 [contents] - 1992
- [j4]David Heckerman, Edward H. Shortliffe:
From certainty factors to belief networks. Artif. Intell. Medicine 4(1): 35-52 (1992) - 1991
- [b1]David Heckerman:
Probabilistic similarity networks. ACM Doctoral dissertation awards, MIT Press 1991, ISBN 978-0-262-08206-8, pp. I-XX, 1-234 - [c17]Dan Geiger, David Heckerman:
Advances in Probabilistic Reasoning. UAI 1991: 118-126 - [c16]David Heckerman, Eric Horvitz, Blackford Middleton:
An Approximate Nonmyopic Computation for Value of Information. UAI 1991: 135-141 - 1990
- [j3]David Heckerman:
Probabilistic similarity networks. Networks 20(5): 607-636 (1990) - [c15]David Heckerman:
Similarity networks for the construction of multiple-faults belief networks. UAI 1990: 51-64 - [c14]Dan Geiger, David Heckerman:
separable and transitive graphoids. UAI 1990: 65-76 - [c13]David Heckerman, Eric Horvitz:
Problem formulation as the reduction of a decision model. UAI 1990: 159-170 - [c12]Henri Jacques Suermondt, Gregory F. Cooper, David Heckerman:
A combination of cutset conditioning with clique-tree propagation in the Pathfinder system. UAI 1990: 245-254
1980 – 1989
- 1989
- [c11]Eric Horvitz, Gregory F. Cooper, David Heckerman:
Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study. IJCAI 1989: 1121-1127 - [c10]David Heckerman:
A Tractable Inference Algorithm for Diagnosing Multiple Diseases. UAI 1989: 163-172 - 1988
- [j2]David Heckerman, Holly Brügge Jimison:
A perspective on confidence and its use in focusing attention during knowledge acquisition. Int. J. Approx. Reason. 2(3): 336 (1988) - [c9]David Heckerman:
An empirical comparison of three inference methods. UAI 1988: 283-302 - 1987
- [j1]Ross D. Shachter, David Heckerman:
Thinking Backward for Knowledge Acquisition. AI Mag. 8(3): 55-61 (1987) - [c8]David Heckerman, Eric Horvitz:
On the Expressiveness of Rule-based Systems for Reasoning with Uncertainty. AAAI 1987: 121-126 - [c7]David Heckerman, Holly Brügge Jimison:
A Bayesian Perspective on Confidence. UAI 1987: 149-160 - 1986
- [c6]Eric Horvitz, David Heckerman, Curtis P. Langlotz:
A Framework for Comparing Alternative Formalisms for Plausible Reasoning. AAAI 1986: 210-214 - [c5]David Heckerman:
An axiomatic framework for belief updates. UAI 1986: 11-22 - [c4]David Heckerman, Eric Horvitz:
The myth of modularity in rule-based systems for reasoning with uncertainty. UAI 1986: 23-34 - [c3]Ross D. Shachter, David Heckerman:
A backwards view for assessment. UAI 1986: 317-324 - 1985
- [c2]Eric Horvitz, David Heckerman:
The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research. UAI 1985: 137-152 - [c1]David Heckerman:
Probabilistic Interpretation for MYCIN's Certainty Factors. UAI 1985: 167-196
Coauthor Index
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