Nir Friedman

	2011
111		Noa Novershtern, Aviv Regev, Nir Friedman: Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinformatics [ISMB/ECCB] 27(13): 177-185 (2011)
110		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)
	2010
109		Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman: Continuous-Time Belief Propagation. ICML 2010: 343-350
108		Ariel Jaimovich, Ruty Rinott, Maya Schuldiner, Hanah Margalit, Nir Friedman: Modularity and directionality in genetic interaction maps. Bioinformatics [ISMB] 26(12): 228-236 (2010)
107		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)
	2009
106		Daphne Koller, Nir Friedman: Probabilistic Graphical Models - Principles and Techniques. MIT Press 2009: I-XXXV, 1-1231
105		Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman: Convexifying the Bethe Free Energy. UAI 2009: 402-410
104		Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman: Mean Field Variational Approximation for Continuous-Time Bayesian Networks. UAI 2009: 91-100
103		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)
	2008
102		Moran Yassour, Tommy Kaplan, Ariel Jaimovich, Nir Friedman: Nucleosome positioning from tiling microarray data. ISMB 2008: 139-146
101		Tal El-Hay, Nir Friedman, Raz Kupferman: Gibbs Sampling in Factorized Continuous-Time Markov Processes. UAI 2008: 169-178
100		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)
99		Helman I. Stern, Ofer Hadar, Nir Friedman: Optimal video stream multiplexing through linear programming. Sig. Proc.: Image Comm. 23(3): 224-238 (2008)
	2007
98		Ilan Wapinski, Avi Pfeffer, Nir Friedman, Aviv Regev: Automatic genome-wide reconstruction of phylogenetic gene trees. ISMB/ECCB (Supplement of Bioinformatics) 2007: 549-558
97		Ariel Jaimovich, Ofer Meshi, Nir Friedman: Template Based Inference in Symmetric Relational Markov Random Fields. UAI 2007: 191-199
96		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)
95		Gal Elidan, Iftach Nachman, Nir Friedman: "Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. Journal of Machine Learning Research 8: 1799-1833 (2007)
	2006
94		Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman: Continuous Time Markov Networks. UAI 2006
93		Nir Friedman, Raz Kupferman: Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. UAI 2006
92		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)
91		Noam Slonim, Nir Friedman, Naftali Tishby: Multivariate Information Bottleneck. Neural Computation 18(8): 1739-1789 (2006)
	2005
90		Itay Mayrose, Nir Friedman, Tal Pupko: A Gamma mixture model better accounts for among site rate heterogeneity. ECCB/JBI 2005: 158
89		Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman: Towards an Integrated Protein-Protein Interaction Network. RECOMB 2005: 14-30
88		Tommy Kaplan, Nir Friedman, Hanah Margalit: Predicting Transcription Factor Binding Sites Using Structural Knowledge. RECOMB 2005: 522-537
87		Yoseph Barash, Gal Elidan, Tommy Kaplan, Nir Friedman: Y. Barash, G. Elidan, T. Kaplan, , N. Friedman. Bioinformatics 21(5): 596-600 (2005)
86		Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman: Learning Module Networks. Journal of Machine Learning Research 6: 557-588 (2005)
85		Gal Elidan, Nir Friedman: Learning Hidden Variable Networks: The Information Bottleneck Approach. Journal of Machine Learning Research 6: 81-127 (2005)
84		Tommy Kaplan, Nir Friedman, Hanah Margalit: Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge. PLoS Computational Biology 1(1): (2005)
	2004
83		Iftach Nachman, Aviv Regev, Nir Friedman: Inferring quantitative models of regulatory networks from expression data. ISMB/ECCB (Supplement of Bioinformatics) 2004: 248-256
82		Iftach Nachman, Gal Elidan, Nir Friedman: "Ideal Parent" Structure Learning for Continuous Variable Networks. UAI 2004: 400-409
81		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)
80		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)
	2003
79		Nir Friedman: Probabilistic models for identifying regulation networks. ECCB 2003: 57
78		Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan: Modeling dependencies in protein-DNA binding sites. RECOMB 2003: 28-37
77		Gal Elidan, Nir Friedman: The Information Bottleneck EM Algorithm. UAI 2003: 200-208
76		Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman: Learning Module Networks. UAI 2003: 525-534
75		Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part I: Foundations CoRR cs.AI/0307070: (2003)
74		Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part II: Revisions and Update CoRR cs.AI/0307071: (2003)
73		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)
	2002
72		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
71		Gal Elidan, Matan Ninio, Nir Friedman, Dale Shuurmans: Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI 2002: 132-139
70		Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller: From promoter sequence to expression: a probabilistic framework. RECOMB 2002: 263-272
69		Noam Slonim, Nir Friedman, Naftali Tishby: Unsupervised document classification using sequential information maximization. SIGIR 2002: 129-136
68		Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer: Robust temporal and spectral modeling for query By melody. SIGIR 2002: 331-338
67		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)
66		Yoseph Barash, Nir Friedman: Context-Specific Bayesian Clustering for Gene Expression Data. Journal of Computational Biology 9(2): 169-191 (2002)
65		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)
64		Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar: Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research 3: 679-707 (2002)
	2001
63		Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar: Learning Probabilistic Models of Relational Structure. ICML 2001: 170-177
62		Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman: Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) 2001: 215-224
61		Eran Segal, Benjamin Taskar, Audrey Gasch, Nir Friedman, Daphne Koller: Rich probabilistic models for gene expression. ISMB (Supplement of Bioinformatics) 2001: 243-252
60		Noam Slonim, Nir Friedman, Naftali Tishby: Agglomerative Multivariate Information Bottleneck. NIPS 2001: 929-936
59		Yoseph Barash, Nir Friedman: Context-specific Bayesian clustering for gene expression data. RECOMB 2001: 12-21
58		Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko: A structural EM algorithm for phylogenetic inference. RECOMB 2001: 132-140
57		Amir Ben-Dor, Nir Friedman, Zohar Yakhini: Class discovery in gene expression data. RECOMB 2001: 31-38
56		Tal El-Hay, Nir Friedman: Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables. UAI 2001: 136-143
55		Gal Elidan, Nir Friedman: Learning the Dimensionality of Hidden Variables. UAI 2001: 144-151
54		Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby: Multivariate Information Bottleneck. UAI 2001: 152-161
53		Yoseph Barash, Gill Bejerano, Nir Friedman: A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. WABI 2001: 278-293
52		Ronen I. Brafman, Nir Friedman: On decision-theoretic foundations for defaults. Artif. Intell. 133(1-2): 1-33 (2001)
51		Nir Friedman, Joseph Y. Halpern: Belief Revision: A Critique CoRR cs.AI/0103020: (2001)
50		Nir Friedman, Joseph Y. Halpern: Plausibility measures and default reasoning. J. ACM 48(4): 648-685 (2001)
	2000
49		Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller: Discovering Hidden Variables: A Structure-Based Approach. NIPS 2000: 479-485
48		Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er: Using Bayesian networks to analyze expression data. RECOMB 2000: 127-135
47		Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini: Tissue classification with gene expression profiles. RECOMB 2000: 54-64
46		Nir Friedman, Dan Geiger, Noam Lotner: Likelihood Computations Using Value Abstraction. UAI 2000: 192-200
45		Nir Friedman, Daphne Koller: Being Bayesian about Network Structure. UAI 2000: 201-210
44		Nir Friedman, Iftach Nachman: Gaussian Process Networks. UAI 2000: 211-219
43		Nir Friedman, Joseph Y. Halpern, Daphne Koller: First-order conditional logic for default reasoning revisited. ACM Trans. Comput. Log. 1(2): 175-207 (2000)
42		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)
41		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)
	1999
40		Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer: Learning Probabilistic Relational Models. IJCAI 1999: 1300-1309
39		Joseph Y. Halpern, Nir Friedman: Plausibility Measures and Default Reasoning: An Overview. LICS 1999: 130-135
38		Richard Dearden, Nir Friedman, David Andre: Model based Bayesian Exploration. UAI 1999: 150-159
37		Nir Friedman, Moisés Goldszmidt, Abraham Wyner: Data Analysis with Bayesian Networks: A Bootstrap Approach. UAI 1999: 196-205
36		Nir Friedman, Iftach Nachman, Dana Pe'er: Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. UAI 1999: 206-215
35		Xavier Boyen, Nir Friedman, Daphne Koller: Discovering the Hidden Structure of Complex Dynamic Systems. UAI 1999: 91-100
34		Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part II: Revision and Update CoRR cs.AI/9903016: (1999)
33		Nir Friedman, Joseph Y. Halpern: Modeling Belief in Dynamic Systems, Part II: Revision and Update. J. Artif. Intell. Res. (JAIR) 10: 117-167 (1999)
32		Nir Friedman, Joseph Y. Halpern: Belief Revision: A Critique. Journal of Logic, Language and Information 8(4): 401-420 (1999)
	1998
31		Craig Boutilier, Nir Friedman, Joseph Y. Halpern: Belief Revision with Unreliable Observations. AAAI/IAAI 1998: 127-134
30		Nir Friedman, Daphne Koller, Avi Pfeffer: Structured Representation of Complex Stochastic Systems. AAAI/IAAI 1998: 157-164
29		Richard Dearden, Nir Friedman, Stuart J. Russell: Bayesian Q-Learning. AAAI/IAAI 1998: 761-768
28		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
27		Nir Friedman, Yoram Singer: Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS 1998: 417-423
26		Nir Friedman: The Bayesian Structural EM Algorithm. UAI 1998: 129-138
25		Nir Friedman, Kevin P. Murphy, Stuart J. Russell: Learning the Structure of Dynamic Probabilistic Networks. UAI 1998: 139-147
24		Nir Friedman, Joseph Y. Halpern, Daphne Koller: First-Order Conditional Logic Revisited CoRR cs.AI/9808005: (1998)
23		Nir Friedman, Joseph Y. Halpern: Plausibility Measures and Default Reasoning CoRR cs.AI/9808007: (1998)
	1997
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. Artif. Intell. 95(2): 257-316 (1997)
16		Nir Friedman, Dan Geiger, Moisés Goldszmidt: Bayesian Network Classifiers. Machine Learning 29(2-3): 131-163 (1997)
	1996
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
	1995
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
	1994
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

Coauthor Index

1	David Andre	[20] [38]
2	Yoseph Barash	[53] [59] [66] [70] [78] [81] [87]
3	Gill Bejerano	[53] [80]
4	Amir Ben-Dor	[42] [47] [57]
5	Craig Boutilier	[10] [31]
6	Xavier Boyen	[35]
7	Ronen I. Brafman	[6] [52]
8	Laurakay Bruhn	[42] [47]
9	Michele E. Clamp	[103]
10	Ido Cohn	[104] [107] [109]
11	Adnan Darwiche	[72]
12	Richard Dearden	[29] [38]
13	Elinor Dehan	[81]
14	Shlomo Dubnov	[68]
15	Tal El-Hay	[56] [94] [101] [104] [107] [109]
16	Gal Elidan	[49] [55] [62] [71] [77] [78] [82] [85] [87] [89] [92] [95]
17	Wilbur Franklin	[81]
18	Manuel Garber	[103]
19	Audrey Gasch	[61]
20	Dan Geiger	[16] [46]
21	Marc Geraci	[81]
22	Lise Getoor	[40] [63] [64]
23	Amir Globerson	[105]
24	Moisés Goldszmidt	[9] [10] [12] [15] [16] [19] [21] [28] [37]
25	Dan Graur	[67]
26	Mitchell Guttman	[103]
27	Naomi Habib	[100] [110]
28	Ofer Hadar	[99]
29	Joseph Y. Halpern	[1] [2] [3] [4] [5] [8] [11] [13] [14] [17] [23] [24] [31] [32] [33] [34] [39] [43] [50] [51] [74] [75]
30	Masami Hasegawa	[67]
31	David Heckerman	[21]
32	Ariel Jaimovich	[89] [92] [97] [102] [105] [108]
33	Naftali Kaminski	[81]
34	Tommy Kaplan	[78] [84] [87] [88] [100] [102]
35	Daphne Koller	[10] [13] [24] [30] [35] [40] [43] [45] [49] [61] [63] [64] [70] [73] [76] [86] [94] [106]
36	Meir Krupsky	[81]
37	Raz Kupferman	[93] [94] [101] [104] [107] [109]
38	Thomas J. Lee	[28]
39	Michal Linial	[41] [48]
40	Noam Lotner	[46] [49]
41	Hanah Margalit	[84] [88] [89] [92] [100] [108]
42	Itay Mayrose	[90]
43	Ofer Meshi	[97] [105]
44	Ori Mosenzon	[54]
45	Kevin P. Murphy	[25]
46	Iftach Nachman	[36] [41] [42] [44] [47] [48] [82] [83] [95]
47	Matan Ninio	[58] [65] [71] [96]
48	Noa Novershtern	[111]
49	Ronald Parr	[20]
50	Dana Pe'er	[36] [41] [48] [62] [76] [86]
51	Itsik Pe'er	[58] [65] [67]
52	Avi Pfeffer	[30] [40] [98]
53	Eyal Privman	[96]
54	Tal Pupko	[58] [65] [67] [90] [96]
55	Aviv Regev	[62] [76] [83] [86] [98] [111]
56	Ruty Rinott	[108]
57	Stuart J. Russell	[18] [21] [25] [29]
58	Maya Schuldiner	[108]
59	Michèl Schummer	[42] [47]
60	Eran Segal	[61] [70] [76] [86]
61	Shai Shalev-Shwartz	[68]
62	Dale Shuurmans	[71]
63	Itamar Simon	[70]
64	Yoram Singer	[27] [68]
65	Julia Sivriver	[110]
66	Noam Slonim	[54] [60] [69] [91]
67	Helman Stern (Helman I. Stern)	[99]
68	Benjamin Taskar (Ben Taskar)	[61] [63] [64]
69	Naftali Tishby	[54] [60] [69] [80] [91]
70	Ilan Wapinski	[98]
71	Abraham Wyner	[37]
72	Xiaohui Xie	[103]
73	Zohar Yakhini	[7] [42] [47] [57]
74	Moran Yassour	[102]
75	Michael C. Zody	[103]

Colors in the list of coauthors

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