| 2008 |
|---|
| 97 | EE | Moran Yassour,
Tommy Kaplan,
Ariel Jaimovich,
Nir Friedman:
Nucleosome positioning from tiling microarray data.
ISMB 2008: 139-146 |
| 96 | EE | Tal El-Hay,
Nir Friedman,
Raz Kupferman:
Gibbs Sampling in Factorized Continuous-Time Markov Processes.
UAI 2008: 169-178 |
| 2007 |
|---|
| 95 | EE | Ilan Wapinski,
Avi Pfeffer,
Nir Friedman,
Aviv Regev:
Automatic genome-wide reconstruction of phylogenetic gene trees.
ISMB/ECCB (Supplement of Bioinformatics) 2007: 549-558 |
| 94 | EE | 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) |
| 2006 |
|---|
| 93 | EE | Tal El-Hay,
Nir Friedman,
Daphne Koller,
Raz Kupferman:
Continuous Time Markov Networks.
UAI 2006 |
| 92 | EE | Nir Friedman,
Raz Kupferman:
Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks.
UAI 2006 |
| 91 | EE | 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) |
| 90 | EE | Noam Slonim,
Nir Friedman,
Naftali Tishby:
Multivariate Information Bottleneck.
Neural Computation 18(8): 1739-1789 (2006) |
| 2005 |
|---|
| 89 | EE | Itay Mayrose,
Nir Friedman,
Tal Pupko:
A Gamma mixture model better accounts for among site rate heterogeneity.
ECCB/JBI 2005: 158 |
| 88 | EE | Ariel Jaimovich,
Gal Elidan,
Hanah Margalit,
Nir Friedman:
Towards an Integrated Protein-Protein Interaction Network.
RECOMB 2005: 14-30 |
| 87 | EE | Tommy Kaplan,
Nir Friedman,
Hanah Margalit:
Predicting Transcription Factor Binding Sites Using Structural Knowledge.
RECOMB 2005: 522-537 |
| 86 | EE | Yoseph Barash,
Gal Elidan,
Tommy Kaplan,
Nir Friedman:
Y. Barash, G. Elidan, T. Kaplan, , N. Friedman.
Bioinformatics 21(5): 596-600 (2005) |
| 85 | EE | Eran Segal,
Dana Pe'er,
Aviv Regev,
Daphne Koller,
Nir Friedman:
Learning Module Networks.
Journal of Machine Learning Research 6: 557-588 (2005) |
| 84 | EE | Gal Elidan,
Nir Friedman:
Learning Hidden Variable Networks: The Information Bottleneck Approach.
Journal of Machine Learning Research 6: 81-127 (2005) |
| 2004 |
|---|
| 83 | EE | Iftach Nachman,
Aviv Regev,
Nir Friedman:
Inferring quantitative models of regulatory networks from expression data.
ISMB/ECCB (Supplement of Bioinformatics) 2004: 248-256 |
| 82 | EE | Iftach Nachman,
Gal Elidan,
Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Networks.
UAI 2004: 400-409 |
| 81 | EE | 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 | EE | 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 | EE | 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 | EE | Nir Friedman,
Joseph Y. Halpern:
Modeling Belief in Dynamic Systems, Part I: Foundations
CoRR cs.AI/0307070: (2003) |
| 74 | EE | 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 | EE | Eran Segal,
Yoseph Barash,
Itamar Simon,
Nir Friedman,
Daphne Koller:
From promoter sequence to expression: a probabilistic framework.
RECOMB 2002: 263-272 |
| 69 | EE | Noam Slonim,
Nir Friedman,
Naftali Tishby:
Unsupervised document classification using sequential information maximization.
SIGIR 2002: 129-136 |
| 68 | EE | 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 | EE | 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 | EE | Noam Slonim,
Nir Friedman,
Naftali Tishby:
Agglomerative Multivariate Information Bottleneck.
NIPS 2001: 929-936 |
| 59 | EE | Yoseph Barash,
Nir Friedman:
Context-specific Bayesian clustering for gene expression data.
RECOMB 2001: 12-21 |
| 58 | EE | Nir Friedman,
Matan Ninio,
Itsik Pe'er,
Tal Pupko:
A structural EM algorithm for phylogenetic inference.
RECOMB 2001: 132-140 |
| 57 | EE | Amir Ben-Dor,
Nir Friedman,
Zohar Yakhini:
Class discovery in gene expression data.
RECOMB 2001: 31-38 |
| 56 | EE | Tal El-Hay,
Nir Friedman:
Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables.
UAI 2001: 136-143 |
| 55 | EE | Gal Elidan,
Nir Friedman:
Learning the Dimensionality of Hidden Variables.
UAI 2001: 144-151 |
| 54 | EE | Nir Friedman,
Ori Mosenzon,
Noam Slonim,
Naftali Tishby:
Multivariate Information Bottleneck.
UAI 2001: 152-161 |
| 53 | EE | Yoseph Barash,
Gill Bejerano,
Nir Friedman:
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites.
WABI 2001: 278-293 |
| 52 | EE | Ronen I. Brafman,
Nir Friedman:
On decision-theoretic foundations for defaults.
Artif. Intell. 133(1-2): 1-33 (2001) |
| 51 | EE | Nir Friedman,
Joseph Y. Halpern:
Belief Revision: A Critique
CoRR cs.AI/0103020: (2001) |
| 50 | EE | 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 | EE | Nir Friedman,
Michal Linial,
Iftach Nachman,
Dana Pe'er:
Using Bayesian networks to analyze expression data.
RECOMB 2000: 127-135 |
| 47 | EE | Amir Ben-Dor,
Laurakay Bruhn,
Nir Friedman,
Iftach Nachman,
Michèl Schummer,
Zohar Yakhini:
Tissue classification with gene expression profiles.
RECOMB 2000: 54-64 |
| 46 | EE | Nir Friedman,
Dan Geiger,
Noam Lotner:
Likelihood Computations Using Value Abstraction.
UAI 2000: 192-200 |
| 45 | EE | Nir Friedman,
Daphne Koller:
Being Bayesian about Network Structure.
UAI 2000: 201-210 |
| 44 | EE | Nir Friedman,
Iftach Nachman:
Gaussian Process Networks.
UAI 2000: 211-219 |
| 43 | EE | 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 | EE | Joseph Y. Halpern,
Nir Friedman:
Plausibility Measures and Default Reasoning: An Overview.
LICS 1999: 130-135 |
| 38 | EE | Richard Dearden,
Nir Friedman,
David Andre:
Model based Bayesian Exploration.
UAI 1999: 150-159 |
| 37 | EE | Nir Friedman,
Moisés Goldszmidt,
Abraham Wyner:
Data Analysis with Bayesian Networks: A Bootstrap Approach.
UAI 1999: 196-205 |
| 36 | EE | Nir Friedman,
Iftach Nachman,
Dana Pe'er:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm.
UAI 1999: 206-215 |
| 35 | EE | Xavier Boyen,
Nir Friedman,
Daphne Koller:
Discovering the Hidden Structure of Complex Dynamic Systems.
UAI 1999: 91-100 |
| 34 | EE | Nir Friedman,
Joseph Y. Halpern:
Modeling Belief in Dynamic Systems, Part II: Revision and Update
CoRR cs.AI/9903016: (1999) |
| 33 | EE | 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 | EE | Nir Friedman,
Yoram Singer:
Efficient Bayesian Parameter Estimation in Large Discrete Domains.
NIPS 1998: 417-423 |
| 26 | EE | Nir Friedman:
The Bayesian Structural EM Algorithm.
UAI 1998: 129-138 |
| 25 | EE | Nir Friedman,
Kevin P. Murphy,
Stuart J. Russell:
Learning the Structure of Dynamic Probabilistic Networks.
UAI 1998: 139-147 |
| 24 | EE | Nir Friedman,
Joseph Y. Halpern,
Daphne Koller:
First-Order Conditional Logic Revisited
CoRR cs.AI/9808005: (1998) |
| 23 | EE | 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 | EE | Nir Friedman,
Moisés Goldszmidt:
Sequential Update of Bayesian Network Structure.
UAI 1997: 165-174 |
| 18 | EE | Nir Friedman,
Stuart J. Russell:
Image Segmentation in Video Sequences: A Probabilistic Approach.
UAI 1997: 175-181 |
| 17 | EE | 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 | EE | Craig Boutilier,
Nir Friedman,
Moisés Goldszmidt,
Daphne Koller:
Context-Specific Independence in Bayesian Networks.
UAI 1996: 115-123 |
| 9 | EE | Nir Friedman,
Moisés Goldszmidt:
Learning Bayesian Networks with Local Structure.
UAI 1996: 252-262 |
| 8 | EE | Nir Friedman,
Joseph Y. Halpern:
A Qualitative Markov Assumption and Its Implications for Belief Change.
UAI 1996: 263-273 |
| 7 | EE | 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 | EE | 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 |
