| 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 | |