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Note: Links lead to the DBLP on the Web. Nir Friedman 69 Yoseph Barash , Gal Elidan , Nir Friedman, Tommy Kaplan : Modeling dependencies in protein-DNA binding sites. RECOMB 2003 : 28-37 68 Nir Friedman, Daphne Koller : Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks. Machine Learning 50 (1-2): 95-125 (2003) 67 Adnan Darwiche , Nir Friedman: UAI '02, Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence, University of Alberta, Edmonton, Alberta, Canada, August 1-4, 2002. Morgan Kaufmann 2002 66 Gal Elidan , Matan Ninio , Nir Friedman, Dale Shuurmans : Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI 2002 : 132-139 65 Eran Segal , Yoseph Barash , Itamar Simon , Nir Friedman, Daphne Koller : From promoter sequence to expression: a probabilistic framework. RECOMB 2002 : 263-272 64 Noam Slonim , Nir Friedman, Naftali Tishby : Unsupervised document classification using sequential information maximization. SIGIR 2002 : 129-136 63 Shai Shalev-Shwartz , Shlomo Dubnov , Nir Friedman, Yoram Singer : Robust temporal and spectral modeling for query By melody. SIGIR 2002 : 331-338 62 Tal Pupko , Itsik Pe'er , Masami Hasegawa , Dan Graur , Nir Friedman: A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families. Bioinformatics 18 (8): 1116-1123 (2002) 61 Yoseph Barash , Nir Friedman: Context-Specific Bayesian Clustering for Gene Expression Data. Journal of Computational Biology 9 (2): 169-191 (2002) 60 Nir Friedman, Matan Ninio , Itsik Pe'er , Tal Pupko : A Structural EM Algorithm for Phylogenetic Inference. Journal of Computational Biology 9 (2): 331-353 (2002) 59 Lise Getoor , Nir Friedman, Daphne Koller , Benjamin Taskar : Learning Probabilistic Models of Relational Structure. ICML 2001 : 170-177 58 Dana Pe'er , Aviv Regev , Gal Elidan , Nir Friedman: Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) 2001 : 215-224 57 Eran Segal , Benjamin Taskar , Audrey Gasch , Nir Friedman, Daphne Koller : Rich probabilistic models for gene expression. ISMB (Supplement of Bioinformatics) 2001 : 243-252 56 Noam Slonim , Nir Friedman, Naftali Tishby : Agglomerative Multivariate Information Bottleneck. NIPS 2001 : 929-936 55 Yoseph Barash , Nir Friedman: Context-specific Bayesian clustering for gene expression data. RECOMB 2001 : 12-21 54 Nir Friedman, Matan Ninio , Itsik Pe'er , Tal Pupko : A structural EM algorithm for phylogenetic inference. RECOMB 2001 : 132-140 53 Amir Ben-Dor , Nir Friedman, Zohar Yakhini : Class discovery in gene expression data. RECOMB 2001 : 31-38 52 Tal El-Hay , Nir Friedman: Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables. UAI 2001 : 136-143 51 Gal Elidan , Nir Friedman: Learning the Dimensionality of Hidden Variables. UAI 2001 : 144-151 50 Nir Friedman, Ori Mosenzon , Noam Slonim , Naftali Tishby : Multivariate Information Bottleneck. UAI 2001 : 152-161 49 Yoseph Barash , Gill Bejerano , Nir Friedman: A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. WABI 2001 : 278-293 48 Ronen I. Brafman , Nir Friedman: On decision-theoretic foundations for defaults. Artificial Intelligence 133 (1-2): 1-33 (2001) 47 Nir Friedman, Joseph Y. Halpern : Plausibility measures and default reasoning. JACM 48 (4): 648-685 (2001) 46 Gal Elidan , Noam Lotner , Nir Friedman, Daphne Koller : Discovering Hidden Variables: A Structure-Based Approach. NIPS 2000 : 479-485 45 Nir Friedman, Michal Linial , Iftach Nachman , Dana Pe'er : Using Bayesian networks to analyze expression data. RECOMB 2000 : 127-135 44 Amir Ben-Dor , Laurakay Bruhn , Nir Friedman, Iftach Nachman , Michèl Schummer , Zohar Yakhini : Tissue classification with gene expression profiles. RECOMB 2000 : 54-64 43 Nir Friedman, Dan Geiger , Noam Lotner : Likelihood Computations Using Value Abstraction. UAI 2000 : 192-200 42 Nir Friedman, Daphne Koller : Being Bayesian about Network Structure. UAI 2000 : 201-210 41 Nir Friedman, Iftach Nachman : Gaussian Process Networks. UAI 2000 : 211-219 40 Amir Ben-Dor , Laurakay Bruhn , Nir Friedman, Iftach Nachman , Michèl Schummer , Zohar Yakhini : Tissue Classification with Gene Expression Profiles. Journal of Computational Biology 7 (3-4): 559-583 (2000) 39 Nir Friedman, Michal Linial , Iftach Nachman , Dana Pe'er : Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology 7 (3-4): 601-620 (2000) 38 Nir Friedman, Joseph Y. Halpern , Daphne Koller : First-order conditional logic for default reasoning revisited. TOCL 1 (2): 175-207 (2000) 37 Nir Friedman, Lise Getoor , Daphne Koller , Avi Pfeffer : Learning Probabilistic Relational Models. IJCAI 1999 : 1300-1309 36 Joseph Y. Halpern , Nir Friedman: Plausibility Measures and Default Reasoning: An Overview. LICS 1999 : 130-135 35 Richard Dearden , Nir Friedman, David Andre : Model based Bayesian Exploration. UAI 1999 : 150-159 34 Nir Friedman, Moisés Goldszmidt , Abraham Wyner : Data Analysis with Bayesian Networks: A Bootstrap Approach. UAI 1999 : 196-205 33 Nir Friedman, Iftach Nachman , Dana Peer : Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. UAI 1999 : 206-215 32 Xavier Boyen , Nir Friedman, Daphne Koller : Discovering the Hidden Structure of Complex Dynamic Systems. UAI 1999 : 91-100 31 Nir Friedman, Joseph Y. Halpern : Modeling Belief in Dynamic Systems, Part II: Revision and Update. JAIR 10 : 117-167 (1999) 30 Nir Friedman, Joseph Y. Halpern : Belief Revision: A Critique. Journal of Logic, Language and Information 8 (4): 401-420 (1999) 29 Craig Boutilier , Nir Friedman, Joseph Y. Halpern : Belief Revision with Unreliable Observations. AAAI/IAAI 1998 : 127-134 28 Nir Friedman, Daphne Koller , Avi Pfeffer : Structured Representation of Complex Stochastic Systems. AAAI/IAAI 1998 : 157-164 27 Richard Dearden , Nir Friedman, Stuart J. Russell : Bayesian Q-Learning. AAAI/IAAI 1998 : 761-768 26 Nir Friedman, Moisés Goldszmidt , Thomas J. Lee : Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting. ICML 1998 : 179-187 25 Nir Friedman, Yoram Singer : Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS 1998 : 417-423 24 Nir Friedman: The Bayesian Structural EM Algorithm. UAI 1998 : 129-138 23 Nir Friedman, Kevin P. Murphy , Stuart J. Russell : Learning the Structure of Dynamic Probabilistic Networks. UAI 1998 : 139-147 22 Nir Friedman: Learning Belief Networks in the Presence of Missing Values and Hidden Variables. ICML 1997 : 125-133 21 Nir Friedman, Moisés Goldszmidt , David Heckerman , Stuart J. Russell : Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) 1997 : 10-15 20 David Andre , Nir Friedman, Ronald Parr : Generalized Prioritized Sweeping. NIPS 1997 19 Nir Friedman, Moisés Goldszmidt : Sequential Update of Bayesian Network Structure. UAI 1997 : 165-174 18 Nir Friedman, Stuart J. Russell : Image Segmentation in Video Sequences: A Probabilistic Approach. UAI 1997 : 175-181 17 Nir Friedman, Joseph Y. Halpern : Modeling Belief in Dynamic Systems, Part I: Foundations. Artificial Intelligence 95 (2): 257-316 (1997) 16 Nir Friedman, Dan Geiger , Moisés Goldszmidt : Bayesian Network Classifiers. Machine Learning 29 (2-3): 131-163 (1997) 15 Nir Friedman, Moisés Goldszmidt : Building Classifiers Using Bayesian Networks. AAAI/IAAI, Vol. 2 1996 : 1277-1284 14 Nir Friedman, Joseph Y. Halpern : Plausibility Measures and Default Reasoning. AAAI/IAAI, Vol. 2 1996 : 1297-1304 13 Nir Friedman, Joseph Y. Halpern , Daphne Koller : First-Order Conditional Logic Revisited. AAAI/IAAI, Vol. 2 1996 : 1305-1312 12 Nir Friedman, Moisés Goldszmidt : Discretizing Continuous Attributes While Learning Bayesian Networks. ICML 1996 : 157-165 11 Nir Friedman, Joseph Y. Halpern : Belief Revision: A Critique. KR 1996 : 421-431 10 Craig Boutilier , Nir Friedman, Moisés Goldszmidt , Daphne Koller : Context-Specific Independence in Bayesian Networks. UAI 1996 : 115-123 9 Nir Friedman, Moisés Goldszmidt : Learning Bayesian Networks with Local Structure. UAI 1996 : 252-262 8 Nir Friedman, Joseph Y. Halpern : A Qualitative Markov Assumption and Its Implications for Belief Change. UAI 1996 : 263-273 7 Nir Friedman, Zohar Yakhini : On the Sample Complexity of Learning Bayesian Networks. UAI 1996 : 274-282 6 Ronen I. Brafman , Nir Friedman: On Decision-Theoretic Foundations for Defaults. IJCAI 1995 : 1458-1465 5 Nir Friedman, Joseph Y. Halpern : Plausibility Measures: A User's Guide. UAI 1995 : 175-184 4 Nir Friedman, Joseph Y. Halpern : Conditional Logics of Belief Change. AAAI 1994 : 915-921 3 Nir Friedman, Joseph Y. Halpern : A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. KR 1994 : 190-201 2 Nir Friedman, Joseph Y. Halpern : On the Complexity of Conditional Logics. KR 1994 : 202-213 1 Nir Friedman, Joseph Y. Halpern : A Knowledge-Based Framework for Belief change, Part I: Foundations. TARK 1994 : 44-64  DiSC'03 © 2003 Association for Computing Machinery |
