David Heckerman

	2009
97		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. Bioinformatics 25(19): 2611-2612 (2009)
	2008
96		Chong Wang, David M. Blei, David Heckerman: Continuous Time Dynamic Topic Models. UAI 2008: 579-586
95		David Heckerman: A Tutorial on Learning with Bayesian Networks. Innovations in Bayesian Networks 2008: 33-82
94		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		Noah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic: Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide Binding. RECOMB 2007: 181-195
92		Joshua Goodman, Gordon V. Cormack, David Heckerman: Spam and the ongoing battle for the inbox. Commun. ACM 50(2): 24-33 (2007)
91		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		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		David Heckerman, Carl Myers Kadie, Jennifer Listgarten: Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. RECOMB 2006: 296-308
88		Francis R. Bach, David Heckerman, Eric Horvitz: Considering Cost Asymmetry in Learning Classifiers. Journal of Machine Learning Research 7: 1713-1741 (2006)
	2005
87		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		Guy Shani, David Heckerman, Ronen I. Brafman: An MDP-Based Recommender System. Journal of Machine Learning Research 6: 1265-1295 (2005)
	2004
84		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		David Heckerman: Graphical models for data mining. KDD 2004: 2
82		Nebojsa Jojic, Vladimir Jojic, David Heckerman: Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences. UAI 2004: 286-292
81		Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek: ARMA Time-Series Modeling with Graphical Models. UAI 2004: 552-560
80		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		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		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		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		David Maxwell Chickering, David Heckerman: Targeted advertising with inventory management. ACM Conference on Electronic Commerce 2000: 145-149
66		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		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		David Maxwell Chickering, David Heckerman: A Decision Theoretic Approach to Targeted Advertising. UAI 2000: 82-88
63		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		David Maxwell Chickering, David Heckerman: Fast Learning from Sparse Data. UAI 1999: 109-115
	1998
61		David Heckerman, Eric Horvitz: Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. UAI 1998: 230-237
60		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		Marina Meila, David Heckerman: An Experimental Comparison of Several Clustering and Initialization Methods. UAI 1998: 386-395
58		John S. Breese, David Heckerman, Carl Myers Kadie: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. UAI 1998: 43-52
57		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		David Heckerman, Christopher Meek: Models and Selection Criteria for Regression and Classification. UAI 1997: 223-228
52		Christopher Meek, David Heckerman: Structure and Parameter Learning for Causal Independence and Causal Interaction Models. UAI 1997: 366-375
51		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		Padhraic Smyth, David Heckerman, Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9(2): 227-269 (1997)
	1996
47		John S. Breese, David Heckerman: Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment. UAI 1996: 124-132
46		David Maxwell Chickering, David Heckerman: Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. UAI 1996: 158-168
45		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		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		Dan Geiger, David Heckerman: A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI 1995: 196-207
39		David Heckerman, Ross D. Shachter: A Definition and Graphical Representation for Causality. UAI 1995: 262-273
38		David Heckerman, Dan Geiger: Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI 1995: 274-284
37		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		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		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		Dan Geiger, David Heckerman: Learning Gaussian Networks. UAI 1994: 235-243
27		David Heckerman, John S. Breese: A New Look at Causal Independence. UAI 1994: 286-292
26		David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI 1994: 293-301
25		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		David Heckerman: Causal Independence for Knowledge Acquisition and Inference. UAI 1993: 122-127
22		Dan Geiger, David Heckerman: Inference Algorithms for Similarity Networks. UAI 1993: 326-334
21		David Heckerman, Michael Shwe: Diagnosis of Multiple Faults: A Sensitivity Analysis. UAI 1993: 80-90
20		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		Dan Geiger, David Heckerman: Advances in Probabilistic Reasoning. UAI 1991: 118-126
18		David Heckerman, Eric Horvitz, Blackford Middleton: An Approximate Nonmyopic Computation for Value of Information. UAI 1991: 135-141
	1990
17		David Heckerman, Eric Horvitz: Problem formulation as the reduction of a decision model. UAI 1990: 159-170
16		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		David Heckerman: Similarity networks for the construction of multiple-faults belief networks. UAI 1990: 51-64
14		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		David Heckerman: A Tractable Inference Algorithm for Diagnosing Multiple Diseases. UAI 1989: 163-172
	1988
11		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		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		David Heckerman: An axiomatic framework for belief updates. UAI 1986: 11-22
4		David Heckerman, Eric Horvitz: The myth of modularity in rule-based systems for reasoning with uncertainty. UAI 1986: 23-34
3		Ross D. Shachter, David Heckerman: A backwards view for assessment. UAI 1986: 317-324
	1985
2		Eric Horvitz, David Heckerman: The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research. UAI 1985: 137-152
1		David Heckerman: Probabilistic Interpretation for MYCIN's Certainty Factors. UAI 1985: 167-196

Coauthor Index

1	Francis R. Bach (Francis Bach)	[88]
2	Tom Berson	[86]
3	David M. Blei	[96]
4	Ronen I. Brafman	[73] [79] [85]
5	Jack S. Breese	[60]
6	John S. Breese	[27] [34] [47] [58]
7	Igor V. Cadez	[66] [77]
8	Jonathan M. Carlson	[97]
9	David Maxwell Chickering (Max Chickering)	[26] [29] [30] [33] [46] [49] [51] [57] [62] [63] [64] [65] [67] [76] [78] [80] [81]
10	Gregory F. Cooper	[13] [16]
11	Gordon V. Cormack	[92]
12	Brendan J. Frey	[71] [87]
13	Nir Friedman	[54]
14	Dan Geiger	[14] [19] [22] [26] [28] [29] [30] [33] [38] [40] [42] [45] [56] [84]
15	Paolo Giudici	[70]
16	Moisés Goldszmidt	[54]
17	Joshua Goodman (Joshua T. Goodman)	[86] [92]
18	David Haussler	[84]
19	Max Henrion	[43]
20	Eric Horvitz	[2] [4] [6] [9] [13] [17] [18] [20] [60] [61] [88]
21	David Hovel	[60]
22	Holly Brügge Jimison	[8] [10]
23	Nebojsa Jojic	[71] [82] [84] [87] [90] [93] [94]
24	Vladimir Jojic	[82] [84] [87]
25	Michael I. Jordan	[48]
26	Carl Myers Kadie	[58] [63] [65] [75] [89] [90] [91]
27	Curtis Langlotz	[6]
28	Bongshin Lee	[97]
29	Jennifer Listgarten	[89] [91]
30	E. H. Mamdani	[24] [32] [36]
31	Heikki Mannila	[55]
32	Christopher Meek	[45] [51] [52] [53] [57] [63] [65] [66] [68] [72] [74] [75] [76] [77] [78] [80] [81] [84] [87]
33	Marina Meila	[59] [69]
34	Blackford Middleton	[18] [20]
35	Lev Nachmanson	[97]
36	Andrew Ng	[86]
37	Daryl Pregibon	[55]
38	Manuel Reyes-Gomez	[90] [93] [94]
39	George G. Robertson	[97]
40	Koos Rommelse	[34] [60]
41	Robert Rounthwaite	[63] [65]
42	Stuart J. Russell	[54]
43	Ora Schueler-Furman	[90]
44	Ross D. Shachter	[3] [7] [25] [31] [39]
45	Guy Shani	[73] [79] [85]
46	Michael Shwe	[21]
47	Adam C. Siepel	[84]
48	Patrice Y. Simard (Patrice Simard)	[71]
49	Padhraic Smyth	[48] [66] [77]
50	Henri Jacques Suermondt	[16] [43]
51	Bo Thiesson	[57] [68] [72] [74] [81]
52	Chong Wang	[96]
53	Michael P. Wellman	[32] [35] [36]
54	Steven White	[66] [77]
55	Joe Whittaker	[70]
56	Noah Zaitlen	[93] [94]

Colors in the list of coauthors