David Heckerman

	2011
110		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
	2009
109		Joel Robertson, Del DeHart, Kristin M. Tolle, David Heckerman: Healthcare delivery in developing countries: challenges and potential solutions. The Fourth Paradigm 2009: 65-73
108		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
107		Chong Wang, David M. Blei, David Heckerman: Continuous Time Dynamic Topic Models. UAI 2008: 579-586
106		David Heckerman: A Tutorial on Learning with Bayesian Networks. Innovations in Bayesian Networks 2008: 33-82
105		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)
104		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 Computational Biology 4(1): (2008)
103		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 Computational Biology 4(11): (2008)
102		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 Computational Biology 4(2): (2008)
	2007
101		Noah Zaitlen, Manuel Reyes-Gomez, David Heckerman, Nebojsa Jojic: Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide Binding. RECOMB 2007: 181-195
100		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
99		Joshua Goodman, Gordon V. Cormack, David Heckerman: Spam and the ongoing battle for the inbox. Commun. ACM 50(2): 24-33 (2007)
98		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)
97		Jennifer Listgarten, Nicole Frahm, Carl Myers Kadie, Christian Brander, David Heckerman: A Statistical Framework for Modeling HLA-Dependent T Cell Response Data. PLoS Computational Biology 3(10): (2007)
96		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 Computational Biology 3(4): (2007)
	2006
95		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
94		David Heckerman, Carl Myers Kadie, Jennifer Listgarten: Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. RECOMB 2006: 296-308
93		Francis R. Bach, David Heckerman, Eric Horvitz: Considering Cost Asymmetry in Learning Classifiers. Journal of Machine Learning Research 7: 1713-1741 (2006)
	2005
92		Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman: Using epitomes to model genetic diversity: Rational design of HIV vaccines. NIPS 2005
91		David Heckerman, Tom Berson, Joshua Goodman, Andrew Y. Ng: The First Conference on E-mail and Anti-Spam. AI Magazine 26(1): 96 (2005)
90		Guy Shani, David Heckerman, Ronen I. Brafman: An MDP-Based Recommender System. Journal of Machine Learning Research 6: 1265-1295 (2005)
	2004
89		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
88		David Heckerman: Graphical models for data mining. KDD 2004: 2
87		Nebojsa Jojic, Vladimir Jojic, David Heckerman: Joint Discovery of Haplotype Blocks and Complex Trait Associations from SNP Sequences. UAI 2004: 286-292
86		Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek: ARMA Time-Series Modeling with Graphical Models. UAI 2004: 552-560
85		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
84		Ronen I. Brafman, David Heckerman, Guy Shani: Recommendation as a Stochastic Sequential Decision Problem. ICAPS 2003: 164-173
83		David Maxwell Chickering, Christopher Meek, David Heckerman: Large-Sample Learning of Bayesian Networks is NP-Hard. UAI 2003: 124-133
82		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
81		Christopher Meek, David Maxwell Chickering, David Heckerman: Autoregressive Tree Models for Time-Series Analysis. SDM 2002
80		Carl Myers Kadie, Christopher Meek, David Heckerman: CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence. UAI 2002: 242-250
79		Christopher Meek, Bo Thiesson, David Heckerman: Staged Mixture Modelling and Boosting. UAI 2002: 335-343
78		Guy Shani, Ronen I. Brafman, David Heckerman: An MDP-based Recommender System. UAI 2002: 453-460
77		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
76		Nebojsa Jojic, Patrice Simard, Brendan J. Frey, David Heckerman: Separating Appearance from Deformation. ICCV 2001: 288-294
75		Paolo Giudici, David Heckerman, Joe Whittaker: Statistical Models for Data Mining. Data Min. Knowl. Discov. 5(3): 163-165 (2001)
74		Marina Meila, David Heckerman: An Experimental Comparison of Model-Based Clustering Methods. Machine Learning 42(1/2): 9-29 (2001)
73		Bo Thiesson, Christopher Meek, David Heckerman: Accelerating EM for Large Databases. Machine Learning 45(3): 279-299 (2001)
	2000
72		David Maxwell Chickering, David Heckerman: Targeted advertising with inventory management. ACM Conference on Electronic Commerce 2000: 145-149
71		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
70		David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie: Dependency Networks for Collaborative Filtering and Data Visualization. UAI 2000: 264-273
69		David Maxwell Chickering, David Heckerman: A Decision Theoretic Approach to Targeted Advertising. UAI 2000: 82-88
68		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
67		David Maxwell Chickering, David Heckerman: Fast Learning from Sparse Data. UAI 1999: 109-115
66		Dan Geiger, David Heckerman: Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions. UAI 1999: 216-225
	1998
65		David Heckerman, Eric Horvitz: Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. UAI 1998: 230-237
64		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
63		Marina Meila, David Heckerman: An Experimental Comparison of Several Clustering and Initialization Methods. UAI 1998: 386-395
62		John S. Breese, David Heckerman, Carl Myers Kadie: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. UAI 1998: 43-52
61		Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman: Learning Mixtures of DAG Models. UAI 1998: 504-513
60		Dan Geiger, David Heckerman: Probabilistic relevance relations. IEEE Transactions on Systems, Man, and Cybernetics, Part A 28(1): 17-25 (1998)
	1997
59		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
58		Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart J. Russell: Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) 1997: 10-15
57		David Heckerman, Christopher Meek: Models and Selection Criteria for Regression and Classification. UAI 1997: 223-228
56		Christopher Meek, David Heckerman: Structure and Parameter Learning for Causal Independence and Causal Interaction Models. UAI 1997: 366-375
55		David Maxwell Chickering, David Heckerman, Christopher Meek: A Bayesian Approach to Learning Bayesian Networks with Local Structure. UAI 1997: 80-89
54		David Heckerman: Bayesian Networks for Data Mining. Data Min. Knowl. Discov. 1(1): 79-119 (1997)
53		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)
52		Padhraic Smyth, David Heckerman, Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9(2): 227-269 (1997)
	1996
51		John S. Breese, David Heckerman: Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment. UAI 1996: 124-132
50		David Maxwell Chickering, David Heckerman: Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network. UAI 1996: 158-168
49		Dan Geiger, David Heckerman, Christopher Meek: Asymptotic Model Selection for Directed Networks with Hidden Variables. UAI 1996: 283-290
48		David Heckerman: Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery and Data Mining 1996: 273-305
47		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
46		Dan Geiger, David Heckerman: Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets. Artif. Intell. 82(1-2): 45-74 (1996)
45		David Heckerman, John S. Breese: Causal independence for probability assessment and inference using Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics, Part A 26(6): 826-831 (1996)
44		John S. Breese, David Heckerman: Decision-theoretic case-based reasoning. IEEE Transactions on Systems, Man, and Cybernetics, Part A 26(6): 838-842 (1996)
	1995
43		David Heckerman: Learning With Bayesian Networks (Abstract). ICML 1995: 588
42		Dan Geiger, David Heckerman: A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. UAI 1995: 196-207
41		David Heckerman, Ross D. Shachter: A Definition and Graphical Representation for Causality. UAI 1995: 262-273
40		David Heckerman, Dan Geiger: Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. UAI 1995: 274-284
39		David Heckerman: A Bayesian Approach to Learning Causal Networks. UAI 1995: 285-295
38		David Heckerman, E. H. Mamdani, Michael P. Wellman: Real-World Applications of Bayesian Networks - Introduction. Commun. ACM 38(3): 24-26 (1995)
37		David Heckerman, Michael P. Wellman: Bayesian Networks. Commun. ACM 38(3): 27-30 (1995)
36		David Heckerman, John S. Breese, Koos Rommelse: Decision-Theoretic Troubleshooting. Commun. ACM 38(3): 49-57 (1995)
35		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)
34		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)
33		David Heckerman, Ross D. Shachter: Decision-Theoretic Foundations for Causal Reasoning. J. Artif. Intell. Res. (JAIR) 3: 405-430 (1995)
32		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
31		David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. KDD Workshop 1994: 85-96
30		Dan Geiger, David Heckerman: Learning Gaussian Networks. UAI 1994: 235-243
29		David Heckerman, John S. Breese: A New Look at Causal Independence. UAI 1994: 286-292
28		David Heckerman, Dan Geiger, David Maxwell Chickering: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. UAI 1994: 293-301
27		David Heckerman, Ross D. Shachter: A Decision-based View of Causality. UAI 1994: 302-310
	1993
26		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
25		David Heckerman: Causal Independence for Knowledge Acquisition and Inference. UAI 1993: 122-127
24		Dan Geiger, David Heckerman: Inference Algorithms for Similarity Networks. UAI 1993: 326-334
23		David Heckerman, Michael Shwe: Diagnosis of Multiple Faults: A Sensitivity Analysis. UAI 1993: 80-90
22		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)
	1992
21		David Heckerman, Edward H. Shortliffe: From certainty factors to belief networks. Artificial Intelligence in Medicine 4(1): 35-52 (1992)
	1991
20		David Heckerman: Probabilistic similarity networks. MIT Press 1991: I-XX, 1-234
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)	[93]
2	Tom Berson	[91]
3	David M. Blei	[107]
4	Ronen I. Brafman	[78] [84] [90]
5	Christian Brander	[97]
6	Jack S. Breese	[64]
7	John S. Breese	[29] [36] [44] [45] [51] [62]
8	Chanson J. Brumme	[103]
9	Zabrina L. Brumme	[102] [103]
10	Igor V. Cadez	[71] [82]
11	Jonathan M. Carlson	[103] [108]
12	Mary Carrington	[102]
13	David Maxwell Chickering (Max Chickering)	[28] [31] [32] [35] [50] [53] [55] [61] [67] [68] [69] [70] [72] [81] [83] [85] [86]
14	Gregory F. Cooper	[13] [16]
15	Gordon V. Cormack	[99]
16	Del DeHart	[109]
17	Wenjie Deng	[96]
18	Nicole Frahm	[97]
19	Brendan J. Frey	[76] [92]
20	Nir Friedman	[58]
21	Dan Geiger	[14] [19] [24] [28] [30] [31] [32] [35] [40] [42] [46] [49] [60] [66] [89]
22	Paolo Giudici	[75]
23	Moisés Goldszmidt	[58]
24	Joshua Goodman (Joshua T. Goodman)	[91] [99]
25	Philip J. R. Goulder	[102] [103]
26	P. Richard Harrigan	[103]
27	David Haussler	[89]
28	Max Henrion	[47]
29	Eric Horvitz	[2] [4] [6] [9] [13] [17] [18] [22] [64] [65] [93]
30	David Hovel	[64]
31	Mark A. Jensen	[96]
32	Holly Brügge Jimison	[8] [10]
33	Nebojsa Jojic	[76] [87] [89] [92] [95] [96] [101] [104] [105]
34	Vladimir Jojic	[87] [89] [92] [104]
35	Michael I. Jordan	[52]
36	Carl Myers Kadie	[62] [68] [70] [80] [94] [95] [97] [98] [102] [103] [110]
37	Darko Kirovski	[104]
38	Curtis P. Langlotz (Curtis Langlotz)	[6]
39	Bongshin Lee	[108]
40	Jennifer Listgarten	[94] [97] [98] [100] [102] [110]
41	E. H. Mamdani (Ebrahim H. Mamdani, Ebrahim Mamdani)	[26] [34] [38]
42	Heikki Mannila	[59]
43	Philippa Matthews	[103]
44	Christopher Meek	[49] [55] [56] [57] [61] [68] [70] [71] [73] [77] [79] [80] [81] [82] [83] [85] [86] [89] [92]
45	Marina Meila	[63] [74]
46	Blackford Middleton	[18] [22]
47	James I. Mullins	[96] [103] [104]
48	Lev Nachmanson	[108]
49	Andrew Y. Ng	[91]
50	David C. Nickle	[96] [104]
51	Sergei L. Kosakovsky Pond	[96] [104]
52	Daryl Pregibon	[59]
53	Manuel Reyes-Gomez	[95] [101] [105]
54	George G. Robertson	[108]
55	Joel Robertson	[109]
56	Morgane Rolland	[96] [104]
57	Koos Rommelse	[36] [64]
58	Robert Rounthwaite	[68] [70]
59	Christine M. Rousseau	[103]
60	Stuart J. Russell	[58]
61	Eric E. Schadt	[110]
62	Ora Schueler-Furman	[95]
63	Mark Seligman	[96]
64	Ross D. Shachter	[3] [7] [27] [33] [41]
65	Guy Shani	[78] [84] [90]
66	Edward H. Shortliffe	[21]
67	Michael Shwe	[23]
68	Adam C. Siepel	[89]
69	Patrice Y. Simard (Patrice Simard)	[76]
70	Padhraic Smyth	[52] [71] [82]
71	Henri Jacques Suermondt	[16] [47]
72	Bo Thiesson	[61] [73] [77] [79] [86]
73	Kristin M. Tolle	[109]
74	Bruce D. Walker	[102] [103]
75	Chong Wang	[107]
76	Michael P. Wellman	[34] [37] [38]
77	Steven White	[71] [82]
78	Joe Whittaker	[75]
79	Gao Xiaojiang	[102]
80	Noah Zaitlen	[101] [105]

Colors in the list of coauthors

Last update Fri May 25 01:42:58 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page