Nir Friedman

Tal El-Hay, Nir Friedman: Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables. CoRR abs/1301.2268 (2013)

Gal Elidan, Nir Friedman: Learning the Dimensionality of Hidden Variables. CoRR abs/1301.2269 (2013)

Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby: Multivariate Information Bottleneck. CoRR abs/1301.2270 (2013)

Nir Friedman, Dan Geiger, Noam Lotner: Likelihood Computations Using Value Abstractions. CoRR abs/1301.3855 (2013)

Nir Friedman, Daphne Koller: Being Bayesian about Network Structure. CoRR abs/1301.3856 (2013)

Nir Friedman, Iftach Nachman: Gaussian Process Networks. CoRR abs/1301.3857 (2013)

Adnan Darwiche, Nir Friedman: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002). CoRR abs/1301.4608 (2013)

Xavier Boyen, Nir Friedman, Daphne Koller: Discovering the Hidden Structure of Complex Dynamic Systems. CoRR abs/1301.6683 (2013)

Richard Dearden, Nir Friedman, David Andre: Model-Based Bayesian Exploration. CoRR abs/1301.6690 (2013)

Nir Friedman, Moisés Goldszmidt, Abraham Wyner: Data Analysis with Bayesian Networks: A Bootstrap Approach. CoRR abs/1301.6695 (2013)

Nir Friedman, Iftach Nachman, Dana Pe'er: Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. CoRR abs/1301.6696 (2013)

Nir Friedman: The Bayesian Structural EM Algorithm. CoRR abs/1301.7373 (2013)

Nir Friedman, Kevin P. Murphy, Stuart J. Russell: Learning the Structure of Dynamic Probabilistic Networks. CoRR abs/1301.7374 (2013)

Nir Friedman, Moisés Goldszmidt: Sequential Update of Bayesian Network Structure. CoRR abs/1302.1538 (2013)

Nir Friedman, Stuart J. Russell: Image Segmentation in Video Sequences: A Probabilistic Approach. CoRR abs/1302.1539 (2013)

Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller: Context-Specific Independence in Bayesian Networks. CoRR abs/1302.3562 (2013)

Nir Friedman, Moisés Goldszmidt: Learning Bayesian Networks with Local Structure. CoRR abs/1302.3577 (2013)

Nir Friedman, Joseph Y. Halpern: A Qualitative Markov Assumption and its Implications for Belief Change. CoRR abs/1302.3578 (2013)

Nir Friedman, Zohar Yakhini: On the Sample Complexity of Learning Bayesian Networks. CoRR abs/1302.3579 (2013)

Nir Friedman, Joseph Y. Halpern: Plausibility Measures: A User's Guide. CoRR abs/1302.4947 (2013)

Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman: Convexifying the Bethe Free Energy. CoRR abs/1205.2624 (2012)

Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman: Mean Field Variational Approximation for Continuous-Time Bayesian Networks. CoRR abs/1205.2655 (2012)

Tal El-Hay, Nir Friedman, Raz Kupferman: Gibbs Sampling in Factorized Continuous-Time Markov Processes. CoRR abs/1206.3251 (2012)

Ariel Jaimovich, Ofer Meshi, Nir Friedman: Template Based Inference in Symmetric Relational Markov Random Fields. CoRR abs/1206.5276 (2012)

Iftach Nachman, Gal Elidan, Nir Friedman: "Ideal Parent" Structure Learning for Continuous Variable Networks. CoRR abs/1207.4133 (2012)

Gal Elidan, Nir Friedman: The Information Bottleneck EM Algorithm. CoRR abs/1212.2460 (2012)

Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman: Learning Module Networks. CoRR abs/1212.2517 (2012)

Noa Novershtern, Aviv Regev, Nir Friedman: Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinformatics [ISMB/ECCB] 27(13): 177-185 (2011)

Julia Sivriver, Naomi Habib, Nir Friedman: An integrative clustering and modeling algorithm for dynamical gene expression data. Bioinformatics [ISMB/ECCB] 27(13): 392-400 (2011)

Ariel Jaimovich, Ruty Rinott, Maya Schuldiner, Hanah Margalit, Nir Friedman: Modularity and directionality in genetic interaction maps. Bioinformatics [ISMB] 26(12): 228-236 (2010)

Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman: Mean Field Variational Approximation for Continuous-Time Bayesian Networks. Journal of Machine Learning Research 11: 2745-2783 (2010)

Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman: Continuous-Time Belief Propagation. ICML 2010: 343-350

Daphne Koller, Nir Friedman: Probabilistic Graphical Models - Principles and Techniques. MIT Press 2009, isbn 978-0-262-01319-2, pp. I-XXXV, 1-1231

Manuel Garber, Mitchell Guttman, Michele E. Clamp, Michael C. Zody, Nir Friedman, Xiaohui Xie: Identifying novel constrained elements by exploiting biased substitution patterns. Bioinformatics 25(12) (2009)

Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman: Mean Field Variational Approximation for Continuous-Time Bayesian Networks. UAI 2009: 91-100

Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman: Convexifying the Bethe Free Energy. UAI 2009: 402-410

Naomi Habib, Tommy Kaplan, Hanah Margalit, Nir Friedman: A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval. PLoS Computational Biology 4(2) (2008)

Helman I. Stern, Ofer Hadar, Nir Friedman: Optimal video stream multiplexing through linear programming. Sig. Proc.: Image Comm. 23(3): 224-238 (2008)

Moran Yassour, Tommy Kaplan, Ariel Jaimovich, Nir Friedman: Nucleosome positioning from tiling microarray data. ISMB 2008: 139-146

Tal El-Hay, Nir Friedman, Raz Kupferman: Gibbs Sampling in Factorized Continuous-Time Markov Processes. UAI 2008: 169-178

Matan Ninio, Eyal Privman, Tal Pupko, Nir Friedman: Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates. Bioinformatics 23(2): 136-141 (2007)

Gal Elidan, Iftach Nachman, Nir Friedman: "Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. Journal of Machine Learning Research 8: 1799-1833 (2007)

Ilan Wapinski, Avi Pfeffer, Nir Friedman, Aviv Regev: Automatic genome-wide reconstruction of phylogenetic gene trees. ISMB/ECCB (Supplement of Bioinformatics) 2007: 549-558

Ariel Jaimovich, Ofer Meshi, Nir Friedman: Template Based Inference in Symmetric Relational Markov Random Fields. UAI 2007: 191-199

Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman: Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach. Journal of Computational Biology 13(2): 145-164 (2006)

Noam Slonim, Nir Friedman, Naftali Tishby: Multivariate Information Bottleneck. Neural Computation 18(8): 1739-1789 (2006)

Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman: Continuous Time Markov Networks. UAI 2006

Nir Friedman, Raz Kupferman: Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. UAI 2006

Yoseph Barash, Gal Elidan, Tommy Kaplan, Nir Friedman: Y. Barash, G. Elidan, T. Kaplan, , N. Friedman. Bioinformatics 21(5): 596-600 (2005)

Gal Elidan, Nir Friedman: Learning Hidden Variable Networks: The Information Bottleneck Approach. Journal of Machine Learning Research 6: 81-127 (2005)

Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman: Learning Module Networks. Journal of Machine Learning Research 6: 557-588 (2005)

Tommy Kaplan, Nir Friedman, Hanah Margalit: Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge. PLoS Computational Biology 1(1) (2005)

Itay Mayrose, Nir Friedman, Tal Pupko: A Gamma mixture model better accounts for among site rate heterogeneity. ECCB/JBI 2005: 158

Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman: Towards an Integrated Protein-Protein Interaction Network. RECOMB 2005: 14-30

Tommy Kaplan, Nir Friedman, Hanah Margalit: Predicting Transcription Factor Binding Sites Using Structural Knowledge. RECOMB 2005: 522-537

Yoseph Barash, Elinor Dehan, Meir Krupsky, Wilbur Franklin, Marc Geraci, Nir Friedman, Naftali Kaminski: Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinformatics 20(6): 839-846 (2004)

Gill Bejerano, Nir Friedman, Naftali Tishby: Efficient Exact p-Value Computation for Small Sample, Sparse, and Surprising Categorical Data. Journal of Computational Biology 11(5): 867-886 (2004)

Iftach Nachman, Aviv Regev, Nir Friedman: Inferring quantitative models of regulatory networks from expression data. ISMB/ECCB (Supplement of Bioinformatics) 2004: 248-256

Iftach Nachman, Gal Elidan, Nir Friedman: "Ideal Parent" Structure Learning for Continuous Variable Networks. UAI 2004: 400-409

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)

Nir Friedman: Probabilistic models for identifying regulation networks. ECCB 2003: 57

Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan: Modeling dependencies in protein-DNA binding sites. RECOMB 2003: 28-37

Gal Elidan, Nir Friedman: The Information Bottleneck EM Algorithm. UAI 2003: 200-208

Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman: Learning Module Networks. UAI 2003: 525-534

Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part I: Foundations. CoRR cs.AI/0307070 (2003)

Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part II: Revisions and Update. CoRR cs.AI/0307071 (2003)

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)

Yoseph Barash, Nir Friedman: Context-Specific Bayesian Clustering for Gene Expression Data. Journal of Computational Biology 9(2): 169-191 (2002)

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)

Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar: Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research 3: 679-707 (2002)

Gal Elidan, Matan Ninio, Nir Friedman, Dale Shuurmans: Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI 2002: 132-139

Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller: From promoter sequence to expression: a probabilistic framework. RECOMB 2002: 263-272

Noam Slonim, Nir Friedman, Naftali Tishby: Unsupervised document classification using sequential information maximization. SIGIR 2002: 129-136

Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer: Robust temporal and spectral modeling for query By melody. SIGIR 2002: 331-338

Adnan Darwiche, Nir Friedman (Eds.): 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, isbn 1-55860-897-4

Ronen I. Brafman, Nir Friedman: On decision-theoretic foundations for defaults. Artif. Intell. 133(1-2): 1-33 (2001)

Nir Friedman, Joseph Y. Halpern: Plausibility measures and default reasoning. J. ACM 48(4): 648-685 (2001)

Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar: Learning Probabilistic Models of Relational Structure. ICML 2001: 170-177

Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman: Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) 2001: 215-224

Eran Segal, Benjamin Taskar, Audrey Gasch, Nir Friedman, Daphne Koller: Rich probabilistic models for gene expression. ISMB (Supplement of Bioinformatics) 2001: 243-252

Noam Slonim, Nir Friedman, Naftali Tishby: Agglomerative Multivariate Information Bottleneck. NIPS 2001: 929-936

Yoseph Barash, Nir Friedman: Context-specific Bayesian clustering for gene expression data. RECOMB 2001: 12-21

Amir Ben-Dor, Nir Friedman, Zohar Yakhini: Class discovery in gene expression data. RECOMB 2001: 31-38

Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko: A structural EM algorithm for phylogenetic inference. RECOMB 2001: 132-140

Tal El-Hay, Nir Friedman: Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables. UAI 2001: 136-143

Gal Elidan, Nir Friedman: Learning the Dimensionality of Hidden Variables. UAI 2001: 144-151

Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby: Multivariate Information Bottleneck. UAI 2001: 152-161

Yoseph Barash, Gill Bejerano, Nir Friedman: A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. WABI 2001: 278-293

Nir Friedman, Joseph Y. Halpern: Belief Revision: A Critique. CoRR cs.AI/0103020 (2001)

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)

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)

Nir Friedman, Joseph Y. Halpern, Daphne Koller: First-order conditional logic for default reasoning revisited. ACM Trans. Comput. Log. 1(2): 175-207 (2000)

Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller: Discovering Hidden Variables: A Structure-Based Approach. NIPS 2000: 479-485

Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini: Tissue classification with gene expression profiles. RECOMB 2000: 54-64

Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er: Using Bayesian networks to analyze expression data. RECOMB 2000: 127-135

Nir Friedman, Dan Geiger, Noam Lotner: Likelihood Computations Using Value Abstraction. UAI 2000: 192-200

Nir Friedman, Daphne Koller: Being Bayesian about Network Structure. UAI 2000: 201-210

Nir Friedman, Iftach Nachman: Gaussian Process Networks. UAI 2000: 211-219

Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part II: Revision and Update. J. Artif. Intell. Res. (JAIR) 10: 117-167 (1999)

Nir Friedman, Joseph Y. Halpern: Belief Revision: A Critique. Journal of Logic, Language and Information 8(4): 401-420 (1999)

Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer: Learning Probabilistic Relational Models. IJCAI 1999: 1300-1309

Joseph Y. Halpern, Nir Friedman: Plausibility Measures and Default Reasoning: An Overview. LICS 1999: 130-135

Xavier Boyen, Nir Friedman, Daphne Koller: Discovering the Hidden Structure of Complex Dynamic Systems. UAI 1999: 91-100

Richard Dearden, Nir Friedman, David Andre: Model based Bayesian Exploration. UAI 1999: 150-159

Nir Friedman, Moisés Goldszmidt, Abraham Wyner: Data Analysis with Bayesian Networks: A Bootstrap Approach. UAI 1999: 196-205

Nir Friedman, Iftach Nachman, Dana Pe'er: Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. UAI 1999: 206-215

Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part II: Revision and Update. CoRR cs.AI/9903016 (1999)

Craig Boutilier, Nir Friedman, Joseph Y. Halpern: Belief Revision with Unreliable Observations. AAAI/IAAI 1998: 127-134

Nir Friedman, Daphne Koller, Avi Pfeffer: Structured Representation of Complex Stochastic Systems. AAAI/IAAI 1998: 157-164

Richard Dearden, Nir Friedman, Stuart J. Russell: Bayesian Q-Learning. AAAI/IAAI 1998: 761-768

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

Nir Friedman, Yoram Singer: Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS 1998: 417-423

Nir Friedman: The Bayesian Structural EM Algorithm. UAI 1998: 129-138

Nir Friedman, Kevin P. Murphy, Stuart J. Russell: Learning the Structure of Dynamic Probabilistic Networks. UAI 1998: 139-147

Nir Friedman, Joseph Y. Halpern, Daphne Koller: First-Order Conditional Logic Revisited. CoRR cs.AI/9808005 (1998)

Nir Friedman, Joseph Y. Halpern: Plausibility Measures and Default Reasoning. CoRR cs.AI/9808007 (1998)

Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part I: Foundations. Artif. Intell. 95(2): 257-316 (1997)

Nir Friedman, Dan Geiger, Moisés Goldszmidt: Bayesian Network Classifiers. Machine Learning 29(2-3): 131-163 (1997)

Nir Friedman: Learning Belief Networks in the Presence of Missing Values and Hidden Variables. ICML 1997: 125-133

Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart J. Russell: Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) 1997: 10-15

David Andre, Nir Friedman, Ronald Parr: Generalized Prioritized Sweeping. NIPS 1997

Nir Friedman, Moisés Goldszmidt: Sequential Update of Bayesian Network Structure. UAI 1997: 165-174

Nir Friedman, Stuart J. Russell: Image Segmentation in Video Sequences: A Probabilistic Approach. UAI 1997: 175-181

Nir Friedman, Moisés Goldszmidt: Building Classifiers Using Bayesian Networks. AAAI/IAAI, Vol. 2 1996: 1277-1284

Nir Friedman, Joseph Y. Halpern: Plausibility Measures and Default Reasoning. AAAI/IAAI, Vol. 2 1996: 1297-1304

Nir Friedman, Joseph Y. Halpern, Daphne Koller: First-Order Conditional Logic Revisited. AAAI/IAAI, Vol. 2 1996: 1305-1312

Nir Friedman, Moisés Goldszmidt: Discretizing Continuous Attributes While Learning Bayesian Networks. ICML 1996: 157-165

Nir Friedman, Joseph Y. Halpern: Belief Revision: A Critique. KR 1996: 421-431

Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller: Context-Specific Independence in Bayesian Networks. UAI 1996: 115-123

Nir Friedman, Moisés Goldszmidt: Learning Bayesian Networks with Local Structure. UAI 1996: 252-262

Nir Friedman, Joseph Y. Halpern: A Qualitative Markov Assumption and Its Implications for Belief Change. UAI 1996: 263-273

Nir Friedman, Zohar Yakhini: On the Sample Complexity of Learning Bayesian Networks. UAI 1996: 274-282

Ronen I. Brafman, Nir Friedman: On Decision-Theoretic Foundations for Defaults. IJCAI 1995: 1458-1465

Nir Friedman, Joseph Y. Halpern: Plausibility Measures: A User's Guide. UAI 1995: 175-184

Nir Friedman, Joseph Y. Halpern: Conditional Logics of Belief Change. AAAI 1994: 915-921

Nir Friedman, Joseph Y. Halpern: A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. KR 1994: 190-201

Nir Friedman, Joseph Y. Halpern: On the Complexity of Conditional Logics. KR 1994: 202-213

Nir Friedman, Joseph Y. Halpern: A Knowledge-Based Framework for Belief change, Part I: Foundations. TARK 1994: 44-64