| 2008 |
|---|
| 96 | EE | Chong Wang,
David M. Blei,
David Heckerman:
Continuous Time Dynamic Topic Models.
UAI 2008: 579-586 |
| 95 | EE | David Heckerman:
A Tutorial on Learning with Bayesian Networks.
Innovations in Bayesian Networks 2008: 33-82 |
| 94 | EE | Noah Zaitlen,
Manuel Reyes-Gomez,
David Heckerman,
Nebojsa Jojic:
Shift-Invariant Adaptive Double Threading: Learning MHC II-Peptide Binding.
Journal of Computational Biology 15(7): 927-942 (2008) |
| 2007 |
|---|
| 93 | EE | Noah Zaitlen,
Manuel Reyes-Gomez,
David Heckerman,
Nebojsa Jojic:
Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide Binding.
RECOMB 2007: 181-195 |
| 92 | EE | Joshua Goodman,
Gordon V. Cormack,
David Heckerman:
Spam and the ongoing battle for the inbox.
Commun. ACM 50(2): 24-33 (2007) |
| 91 | EE | David Heckerman,
Carl Myers Kadie,
Jennifer Listgarten:
Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction.
Journal of Computational Biology 14(6): 736-746 (2007) |
| 2006 |
|---|
| 90 | EE | 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 |
| 89 | EE | David Heckerman,
Carl Myers Kadie,
Jennifer Listgarten:
Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction.
RECOMB 2006: 296-308 |
| 88 | EE | Francis R. Bach,
David Heckerman,
Eric Horvitz:
Considering Cost Asymmetry in Learning Classifiers.
Journal of Machine Learning Research 7: 1713-1741 (2006) |
| 2005 |
|---|
| 87 | EE | Nebojsa Jojic,
Vladimir Jojic,
Brendan J. Frey,
Christopher Meek,
David Heckerman:
Using epitomes to model genetic diversity: Rational design of HIV vaccines.
NIPS 2005 |
| 86 | | David Heckerman,
Tom Berson,
Joshua Goodman,
Andrew Ng:
The First Conference on E-mail and Anti-Spam.
AI Magazine 26(1): 96- (2005) |
| 85 | EE | Guy Shani,
David Heckerman,
Ronen I. Brafman:
An MDP-Based Recommender System.
Journal of Machine Learning Research 6: 1265-1295 (2005) |
| 2004 |
|---|
| 84 | EE | 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 |
| 83 | EE | David Heckerman:
Graphical models for data mining.
KDD 2004: 2 |
| 82 | EE | Nebojsa Jojic,
Vladimir Jojic,
David Heckerman:
Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences.
UAI 2004: 286-292 |
| 81 | EE | Bo Thiesson,
David Maxwell Chickering,
David Heckerman,
Christopher Meek:
ARMA Time-Series Modeling with Graphical Models.
UAI 2004: 552-560 |
| 80 | EE | David Maxwell Chickering,
David Heckerman,
Christopher Meek:
Large-Sample Learning of Bayesian Networks is NP-Hard.
Journal of Machine Learning Research 5: 1287-1330 (2004) |
| 2003 |
|---|
| 79 | | Ronen I. Brafman,
David Heckerman,
Guy Shani:
Recommendation as a Stochastic Sequential Decision Problem.
ICAPS 2003: 164-173 |
| 78 | | David Maxwell Chickering,
Christopher Meek,
David Heckerman:
Large-Sample Learning of Bayesian Networks is NP-Hard.
UAI 2003: 124-133 |
| 77 | EE | 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) |
| 2002 |
|---|
| 76 | EE | Christopher Meek,
David Maxwell Chickering,
David Heckerman:
Autoregressive Tree Models for Time-Series Analysis.
SDM 2002 |
| 75 | | Carl Myers Kadie,
Christopher Meek,
David Heckerman:
CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence.
UAI 2002: 242-250 |
| 74 | | Christopher Meek,
Bo Thiesson,
David Heckerman:
Staged Mixture Modelling and Boosting.
UAI 2002: 335-343 |
| 73 | | Guy Shani,
Ronen I. Brafman,
David Heckerman:
An MDP-based Recommender System.
UAI 2002: 453-460 |
| 72 | EE | Christopher Meek,
Bo Thiesson,
David Heckerman:
The Learning-Curve Sampling Method Applied to Model-Based Clustering.
Journal of Machine Learning Research 2: 397-418 (2002) |
| 2001 |
|---|
| 71 | | Nebojsa Jojic,
Patrice Simard,
Brendan J. Frey,
David Heckerman:
Separating Appearance from Deformation.
ICCV 2001: 288-294 |
| 70 | | Paolo Giudici,
David Heckerman,
Joe Whittaker:
Statistical Models for Data Mining.
Data Min. Knowl. Discov. 5(3): 163-165 (2001) |
| 69 | | Marina Meila,
David Heckerman:
An Experimental Comparison of Model-Based Clustering Methods.
Machine Learning 42(1/2): 9-29 (2001) |
| 68 | | Bo Thiesson,
Christopher Meek,
David Heckerman:
Accelerating EM for Large Databases.
Machine Learning 45(3): 279-299 (2001) |
| 2000 |
|---|
| 67 | EE | David Maxwell Chickering,
David Heckerman:
Targeted advertising with inventory management.
ACM Conference on Electronic Commerce 2000: 145-149 |
| 66 | EE | 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 |
| 65 | EE | David Heckerman,
David Maxwell Chickering,
Christopher Meek,
Robert Rounthwaite,
Carl Myers Kadie:
Dependency Networks for Collaborative Filtering and Data Visualization.
UAI 2000: 264-273 |
| 64 | EE | David Maxwell Chickering,
David Heckerman:
A Decision Theoretic Approach to Targeted Advertising.
UAI 2000: 82-88 |
| 63 | EE | David Heckerman,
David Maxwell Chickering,
Christopher Meek,
Robert Rounthwaite,
Carl Myers Kadie:
Dependency Networks for Inference, Collaborative Filtering, and Data Visualization.
Journal of Machine Learning Research 1: 49-75 (2000) |
| 1999 |
|---|
| 62 | EE | David Maxwell Chickering,
David Heckerman:
Fast Learning from Sparse Data.
UAI 1999: 109-115 |
| 1998 |
|---|
| 61 | EE | David Heckerman,
Eric Horvitz:
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach.
UAI 1998: 230-237 |
| 60 | EE | 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 |
| 59 | EE | Marina Meila,
David Heckerman:
An Experimental Comparison of Several Clustering and Initialization Methods.
UAI 1998: 386-395 |
| 58 | EE | John S. Breese,
David Heckerman,
Carl Myers Kadie:
Empirical Analysis of Predictive Algorithms for Collaborative Filtering.
UAI 1998: 43-52 |
| 57 | EE | Bo Thiesson,
Christopher Meek,
David Maxwell Chickering,
David Heckerman:
Learning Mixtures of DAG Models.
UAI 1998: 504-513 |
| 56 | | Dan Geiger,
David Heckerman:
Probabilistic relevance relations.
IEEE Transactions on Systems, Man, and Cybernetics, Part A 28(1): 17-25 (1998) |
| 1997 |
|---|
| 55 | | 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 |
| 54 | | Nir Friedman,
Moisés Goldszmidt,
David Heckerman,
Stuart J. Russell:
Challenge: What is the Impact of Bayesian Networks on Learning?
IJCAI (1) 1997: 10-15 |
| 53 | EE | David Heckerman,
Christopher Meek:
Models and Selection Criteria for Regression and Classification.
UAI 1997: 223-228 |
| 52 | EE | Christopher Meek,
David Heckerman:
Structure and Parameter Learning for Causal Independence and Causal Interaction Models.
UAI 1997: 366-375 |
| 51 | EE | David Maxwell Chickering,
David Heckerman,
Christopher Meek:
A Bayesian Approach to Learning Bayesian Networks with Local Structure.
UAI 1997: 80-89 |
| 50 | | David Heckerman:
Bayesian Networks for Data Mining.
Data Min. Knowl. Discov. 1(1): 79-119 (1997) |
| 49 | | David Maxwell Chickering,
David Heckerman:
Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables.
Machine Learning 29(2-3): 181-212 (1997) |
| 48 | EE | Padhraic Smyth,
David Heckerman,
Michael I. Jordan:
Probabilistic Independence Networks for Hidden Markov Probability Models.
Neural Computation 9(2): 227-269 (1997) |
| 1996 |
|---|
| 47 | EE | John S. Breese,
David Heckerman:
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment.
UAI 1996: 124-132 |
| 46 | EE | David Maxwell Chickering,
David Heckerman:
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network.
UAI 1996: 158-168 |
| 45 | EE | Dan Geiger,
David Heckerman,
Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables.
UAI 1996: 283-290 |
| 44 | | David Heckerman:
Bayesian Networks for Knowledge Discovery.
Advances in Knowledge Discovery and Data Mining 1996: 273-305 |
| 43 | | 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 |
| 42 | EE | Dan Geiger,
David Heckerman:
Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets.
Artif. Intell. 82(1-2): 45-74 (1996) |
| 1995 |
|---|
| 41 | | David Heckerman:
Learning With Bayesian Networks (Abstract).
ICML 1995: 588 |
| 40 | EE | Dan Geiger,
David Heckerman:
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks.
UAI 1995: 196-207 |
| 39 | EE | David Heckerman,
Ross D. Shachter:
A Definition and Graphical Representation for Causality.
UAI 1995: 262-273 |
| 38 | EE | David Heckerman,
Dan Geiger:
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains.
UAI 1995: 274-284 |
| 37 | EE | David Heckerman:
A Bayesian Approach to Learning Causal Networks.
UAI 1995: 285-295 |
| 36 | | David Heckerman,
E. H. Mamdani,
Michael P. Wellman:
Real-World Applications of Bayesian Networks - Introduction.
Commun. ACM 38(3): 24-26 (1995) |
| 35 | | David Heckerman,
Michael P. Wellman:
Bayesian Networks.
Commun. ACM 38(3): 27-30 (1995) |
| 34 | | David Heckerman,
John S. Breese,
Koos Rommelse:
Decision-Theoretic Troubleshooting.
Commun. ACM 38(3): 49-57 (1995) |
| 33 | EE | 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) |
| 32 | EE | 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) |
| 31 | | David Heckerman,
Ross D. Shachter:
Decision-Theoretic Foundations for Causal Reasoning.
J. Artif. Intell. Res. (JAIR) 3: 405-430 (1995) |
| 30 | | David Heckerman,
Dan Geiger,
David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
Machine Learning 20(3): 197-243 (1995) |
| 1994 |
|---|
| 29 | | David Heckerman,
Dan Geiger,
David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
KDD Workshop 1994: 85-96 |
| 28 | EE | Dan Geiger,
David Heckerman:
Learning Gaussian Networks.
UAI 1994: 235-243 |
| 27 | EE | David Heckerman,
John S. Breese:
A New Look at Causal Independence.
UAI 1994: 286-292 |
| 26 | EE | David Heckerman,
Dan Geiger,
David Maxwell Chickering:
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.
UAI 1994: 293-301 |
| 25 | EE | David Heckerman,
Ross D. Shachter:
A Decision-based View of Causality.
UAI 1994: 302-310 |
| 1993 |
|---|
| 24 | | David Heckerman,
E. H. Mamdani:
UAI '93: Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence, July 9-11, 1993, The Catholic University of America, Providence, Washington, DC, USA
Morgan Kaufmann 1993 |
| 23 | EE | David Heckerman:
Causal Independence for Knowledge Acquisition and Inference.
UAI 1993: 122-127 |
| 22 | EE | Dan Geiger,
David Heckerman:
Inference Algorithms for Similarity Networks.
UAI 1993: 326-334 |
| 21 | EE | David Heckerman,
Michael Shwe:
Diagnosis of Multiple Faults: A Sensitivity Analysis.
UAI 1993: 80-90 |
| 20 | EE | 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) |
| 1991 |
|---|
| 19 | EE | Dan Geiger,
David Heckerman:
Advances in Probabilistic Reasoning.
UAI 1991: 118-126 |
| 18 | EE | David Heckerman,
Eric Horvitz,
Blackford Middleton:
An Approximate Nonmyopic Computation for Value of Information.
UAI 1991: 135-141 |
| 1990 |
|---|
| 17 | EE | David Heckerman,
Eric Horvitz:
Problem formulation as the reduction of a decision model.
UAI 1990: 159-170 |
| 16 | EE | 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 |
| 15 | EE | David Heckerman:
Similarity networks for the construction of multiple-faults belief networks.
UAI 1990: 51-64 |
| 14 | EE | Dan Geiger,
David Heckerman:
separable and transitive graphoids.
UAI 1990: 65-76 |
| 1989 |
|---|
| 13 | | Eric Horvitz,
Gregory F. Cooper,
David Heckerman:
Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study.
IJCAI 1989: 1121-1127 |
| 12 | EE | David Heckerman:
A Tractable Inference Algorithm for Diagnosing Multiple Diseases.
UAI 1989: 163-172 |
| 1988 |
|---|
| 11 | EE | David Heckerman:
An empirical comparison of three inference methods.
UAI 1988: 283-302 |
| 10 | | David Heckerman,
Holly Brügge Jimison:
A perspective on confidence and its use in focusing attention during knowledge acquisition.
Int. J. Approx. Reasoning 2(3): 336 (1988) |
| 1987 |
|---|
| 9 | | David Heckerman,
Eric Horvitz:
On the Expressiveness of Rule-based Systems for Reasoning with Uncertainty.
AAAI 1987: 121-126 |
| 8 | EE | David Heckerman,
Holly Brügge Jimison:
A Bayesian Perspective on Confidence.
UAI 1987: 149-160 |
| 7 | | Ross D. Shachter,
David Heckerman:
Thinking Backward for Knowledge Acquisition.
AI Magazine 8(3): 55-61 (1987) |
| 1986 |
|---|
| 6 | | Eric Horvitz,
David Heckerman,
Curtis Langlotz:
A Framework for Comparing Alternative Formalisms for Plausible Reasoning.
AAAI 1986: 210-214 |
| 5 | EE | David Heckerman:
An axiomatic framework for belief updates.
UAI 1986: 11-22 |
| 4 | EE | David Heckerman,
Eric Horvitz:
The myth of modularity in rule-based systems for reasoning with uncertainty.
UAI 1986: 23-34 |
| 3 | EE | Ross D. Shachter,
David Heckerman:
A backwards view for assessment.
UAI 1986: 317-324 |
| 1985 |
|---|
| 2 | EE | Eric Horvitz,
David Heckerman:
The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research.
UAI 1985: 137-152 |
| 1 | EE | David Heckerman:
Probabilistic Interpretation for MYCIN's Certainty Factors.
UAI 1985: 167-196 |
