default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDNir Friedman
Person information
- affiliation: Hebrew University of Jerusalem, Israel
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2010 – 2019
- 2019
[j35]Mor Nitzan
, Nikos Karaiskos, Nir Friedman, Nikolaus Rajewsky:
Gene expression cartography. Nat. 576(7785): 132-137 (2019)- 2017
- [j34]Nili Tickotsky, Tal Sagiv, Jaime Prilusky, Eric Shifrut, Nir Friedman:
McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinform. 33(18): 2924-2929 (2017) - 2014
- [j33]Niclas Thomas, Katharine Best, Mattia Cinelli, Shlomit Reich-Zeliger, Hilah Gal, Eric Shifrut, Asaf Madi, Nir Friedman, John Shawe-Taylor, Benny Chain:
Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinform. 30(22): 3181-3188 (2014) - 2013
- [i35]Tal El-Hay, Nir Friedman:
Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables. CoRR abs/1301.2268 (2013) - [i34]Gal Elidan, Nir Friedman:
Learning the Dimensionality of Hidden Variables. CoRR abs/1301.2269 (2013) - [i33]Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby:
Multivariate Information Bottleneck. CoRR abs/1301.2270 (2013) - [i32]Nir Friedman, Dan Geiger, Noam Lotner:
Likelihood Computations Using Value Abstractions. CoRR abs/1301.3855 (2013) - [i31]Nir Friedman, Daphne Koller:
Being Bayesian about Network Structure. CoRR abs/1301.3856 (2013) - [i30]Nir Friedman, Iftach Nachman:
Gaussian Process Networks. CoRR abs/1301.3857 (2013) - [i29]Adnan Darwiche, Nir Friedman:
Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002). CoRR abs/1301.4608 (2013) - [i28]Xavier Boyen, Nir Friedman, Daphne Koller:
Discovering the Hidden Structure of Complex Dynamic Systems. CoRR abs/1301.6683 (2013) - [i27]Richard Dearden, Nir Friedman, David Andre:
Model-Based Bayesian Exploration. CoRR abs/1301.6690 (2013) - [i26]Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner:
Data Analysis with Bayesian Networks: A Bootstrap Approach. CoRR abs/1301.6695 (2013) - [i25]Nir Friedman, Iftach Nachman, Dana Pe'er:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. CoRR abs/1301.6696 (2013) - [i24]Nir Friedman:
The Bayesian Structural EM Algorithm. CoRR abs/1301.7373 (2013) - [i23]Nir Friedman, Kevin P. Murphy, Stuart Russell:
Learning the Structure of Dynamic Probabilistic Networks. CoRR abs/1301.7374 (2013) - [i22]Nir Friedman, Moisés Goldszmidt:
Sequential Update of Bayesian Network Structure. CoRR abs/1302.1538 (2013) - [i21]Nir Friedman, Stuart Russell:
Image Segmentation in Video Sequences: A Probabilistic Approach. CoRR abs/1302.1539 (2013) - [i20]Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller:
Context-Specific Independence in Bayesian Networks. CoRR abs/1302.3562 (2013) - [i19]Nir Friedman, Moisés Goldszmidt:
Learning Bayesian Networks with Local Structure. CoRR abs/1302.3577 (2013) - [i18]Nir Friedman, Joseph Y. Halpern:
A Qualitative Markov Assumption and its Implications for Belief Change. CoRR abs/1302.3578 (2013) - [i17]Nir Friedman, Zohar Yakhini:
On the Sample Complexity of Learning Bayesian Networks. CoRR abs/1302.3579 (2013) - [i16]Nir Friedman, Joseph Y. Halpern:
Plausibility Measures: A User's Guide. CoRR abs/1302.4947 (2013) - 2012
- [j32]Yonatan Savir, Nir Waysbort, Yaron E. Antebi, Tsvi Tlusty, Nir Friedman:
Balancing speed and accuracy of polyclonal T cell activation: a role for extracellular feedback. BMC Syst. Biol. 6: 111 (2012) - [i15]Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman:
Convexifying the Bethe Free Energy. CoRR abs/1205.2624 (2012) - [i14]Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman:
Mean Field Variational Approximation for Continuous-Time Bayesian Networks. CoRR abs/1205.2655 (2012) - [i13]Tal El-Hay, Nir Friedman, Raz Kupferman:
Gibbs Sampling in Factorized Continuous-Time Markov Processes. CoRR abs/1206.3251 (2012) - [i12]Ariel Jaimovich, Ofer Meshi, Nir Friedman:
Template Based Inference in Symmetric Relational Markov Random Fields. CoRR abs/1206.5276 (2012) - [i11]Nir Friedman, Raz Kupferman:
Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. CoRR abs/1206.6835 (2012) - [i10]Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman:
Continuous Time Markov Networks. CoRR abs/1206.6838 (2012) - [i9]Iftach Nachman, Gal Elidan, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Networks. CoRR abs/1207.4133 (2012) - [i8]Gal Elidan, Nir Friedman:
The Information Bottleneck EM Algorithm. CoRR abs/1212.2460 (2012) - [i7]Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. CoRR abs/1212.2517 (2012) - 2011
- [j31]Noa Novershtern, Aviv Regev, Nir Friedman:
Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinform. 27(13): 177-185 (2011) - [j30]Julia Sivriver, Naomi Habib, Nir Friedman:
An integrative clustering and modeling algorithm for dynamical gene expression data. Bioinform. 27(13): 392-400 (2011) - 2010
- [j29]Ariel Jaimovich, Ruty Rinott, Maya Schuldiner, Hanah Margalit, Nir Friedman:
Modularity and directionality in genetic interaction maps. Bioinform. 26(12): 228-236 (2010) - [j28]Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman:
Mean Field Variational Approximation for Continuous-Time Bayesian Networks. J. Mach. Learn. Res. 11: 2745-2783 (2010) - [c74]Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman:
Continuous-Time Belief Propagation. ICML 2010: 343-350
2000 – 2009
- 2009
- [b2]Daphne Koller, Nir Friedman:
Probabilistic Graphical Models - Principles and Techniques. MIT Press 2009, ISBN 978-0-262-01319-2, pp. I-XXXV, 1-1231 - [j27]Manuel Garber, Mitchell Guttman, Michele E. Clamp, Michael C. Zody, Nir Friedman, Xiaohui Xie:
Identifying novel constrained elements by exploiting biased substitution patterns. Bioinform. 25(12) (2009) - [c73]Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman:
Mean Field Variational Approximation for Continuous-Time Bayesian Networks. UAI 2009: 91-100 - [c72]Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman:
Convexifying the Bethe Free Energy. UAI 2009: 402-410 - 2008
- [j26]Naomi Habib, Tommy Kaplan, Hanah Margalit, Nir Friedman:
A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval. PLoS Comput. Biol. 4(2) (2008) - [j25]Helman I. Stern, Ofer Hadar, Nir Friedman:
Optimal video stream multiplexing through linear programming. Signal Process. Image Commun. 23(3): 224-238 (2008) - [c71]Moran Yassour, Tommy Kaplan, Ariel Jaimovich, Nir Friedman:
Nucleosome positioning from tiling microarray data. ISMB 2008: 139-146 - [c70]Tal El-Hay, Nir Friedman, Raz Kupferman:
Gibbs Sampling in Factorized Continuous-Time Markov Processes. UAI 2008: 169-178 - 2007
- [j24]Matan Ninio, Eyal Privman, Tal Pupko, Nir Friedman:
Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates. Bioinform. 23(2): 136-141 (2007) - [j23]Gal Elidan, Iftach Nachman, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. J. Mach. Learn. Res. 8: 1799-1833 (2007) - [c69]Ilan Wapinski, Avi Pfeffer, Nir Friedman, Aviv Regev:
Automatic genome-wide reconstruction of phylogenetic gene trees. ISMB/ECCB (Supplement of Bioinformatics) 2007: 549-558 - [c68]Ariel Jaimovich, Ofer Meshi, Nir Friedman:
Template Based Inference in Symmetric Relational Markov Random Fields. UAI 2007: 191-199 - 2006
- [j22]Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman:
Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach. J. Comput. Biol. 13(2): 145-164 (2006) - [j21]Noam Slonim, Nir Friedman, Naftali Tishby:
Multivariate Information Bottleneck. Neural Comput. 18(8): 1739-1789 (2006) - [c67]Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman:
Continuous Time Markov Networks. UAI 2006 - [c66]Nir Friedman, Raz Kupferman:
Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. UAI 2006 - 2005
- [j20]Yoseph Barash, Gal Elidan, Tommy Kaplan, Nir Friedman:
CIS: compound importance sampling method for protein-DNA binding site p-value estimation. Bioinform. 21(5): 596-600 (2005) - [j19]Gal Elidan, Nir Friedman:
Learning Hidden Variable Networks: The Information Bottleneck Approach. J. Mach. Learn. Res. 6: 81-127 (2005) - [j18]Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. J. Mach. Learn. Res. 6: 557-588 (2005) - [j17]Tommy Kaplan, Nir Friedman, Hanah Margalit:
Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge. PLoS Comput. Biol. 1(1) (2005) - [c65]Itay Mayrose, Nir Friedman, Tal Pupko:
A Gamma mixture model better accounts for among site rate heterogeneity. ECCB/JBI 2005: 158 - [c64]Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman:
Towards an Integrated Protein-Protein Interaction Network. RECOMB 2005: 14-30 - [c63]Tommy Kaplan, Nir Friedman, Hanah Margalit:
Predicting Transcription Factor Binding Sites Using Structural Knowledge. RECOMB 2005: 522-537 - 2004
- [j16]Yoseph Barash, Elinor Dehan, Meir Krupsky, Wilbur Franklin, Marc Geraci, Nir Friedman, Naftali Kaminski:
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinform. 20(6): 839-846 (2004) - [j15]Gill Bejerano, Nir Friedman, Naftali Tishby:
Efficient Exact p-Value Computation for Small Sample, Sparse, and Surprising Categorical Data. J. Comput. Biol. 11(5): 867-886 (2004) - [c62]Iftach Nachman, Aviv Regev, Nir Friedman:
Inferring quantitative models of regulatory networks from expression data. ISMB/ECCB (Supplement of Bioinformatics) 2004: 248-256 - [c61]Iftach Nachman, Gal Elidan, Nir Friedman:
"Ideal Parent" Structure Learning for Continuous Variable Networks. UAI 2004: 400-409 - 2003
- [j14]Nir Friedman, Daphne Koller:
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks. Mach. Learn. 50(1-2): 95-125 (2003) - [c60]Nir Friedman:
Probabilistic models for identifying regulation networks. ECCB 2003: 57 - [c59]Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan:
Modeling dependencies in protein-DNA binding sites. RECOMB 2003: 28-37 - [c58]Gal Elidan, Nir Friedman:
The Information Bottleneck EM Algorithm. UAI 2003: 200-208 - [c57]Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. UAI 2003: 525-534 - [i6]Nir Friedman, Joseph Y. Halpern:
Modeling Belief in Dynamic Systems, Part I: Foundations. CoRR cs.AI/0307070 (2003) - [i5]Nir Friedman, Joseph Y. Halpern:
Modeling Belief in Dynamic Systems, Part II: Revisions and Update. CoRR cs.AI/0307071 (2003) - 2002
- [j13]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. Bioinform. 18(8): 1116-1123 (2002) - [j12]Yoseph Barash, Nir Friedman:
Context-Specific Bayesian Clustering for Gene Expression Data. J. Comput. Biol. 9(2): 169-191 (2002) - [j11]Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko:
A Structural EM Algorithm for Phylogenetic Inference. J. Comput. Biol. 9(2): 331-353 (2002) - [j10]Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar:
Learning Probabilistic Models of Link Structure. J. Mach. Learn. Res. 3: 679-707 (2002) - [c56]Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans:
Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI 2002: 132-139 - [c55]Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller:
From promoter sequence to expression: a probabilistic framework. RECOMB 2002: 263-272 - [c54]Noam Slonim, Nir Friedman, Naftali Tishby:
Unsupervised document classification using sequential information maximization. SIGIR 2002: 129-136 - [c53]Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer:
Robust temporal and spectral modeling for query By melody. SIGIR 2002: 331-338 - [e1]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, ISBN 1-55860-897-4 [contents] - 2001
- [j9]Ronen I. Brafman, Nir Friedman:
On decision-theoretic foundations for defaults. Artif. Intell. 133(1-2): 1-33 (2001) - [j8]Nir Friedman, Joseph Y. Halpern:
Plausibility measures and default reasoning. J. ACM 48(4): 648-685 (2001) - [c52]Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar:
Learning Probabilistic Models of Relational Structure. ICML 2001: 170-177 - [c51]Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman:
Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) 2001: 215-224 - [c50]Eran Segal, Benjamin Taskar, Audrey P. Gasch, Nir Friedman, Daphne Koller:
Rich probabilistic models for gene expression. ISMB (Supplement of Bioinformatics) 2001: 243-252 - [c49]Noam Slonim, Nir Friedman, Naftali Tishby:
Agglomerative Multivariate Information Bottleneck. NIPS 2001: 929-936 - [c48]Yoseph Barash, Nir Friedman:
Context-specific Bayesian clustering for gene expression data. RECOMB 2001: 12-21 - [c47]Amir Ben-Dor, Nir Friedman, Zohar Yakhini:
Class discovery in gene expression data. RECOMB 2001: 31-38 - [c46]Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko:
A structural EM algorithm for phylogenetic inference. RECOMB 2001: 132-140 - [c45]Tal El-Hay, Nir Friedman:
Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables. UAI 2001: 136-143 - [c44]Gal Elidan, Nir Friedman:
Learning the Dimensionality of Hidden Variables. UAI 2001: 144-151 - [c43]Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby:
Multivariate Information Bottleneck. UAI 2001: 152-161 - [c42]Yoseph Barash, Gill Bejerano, Nir Friedman:
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. WABI 2001: 278-293 - [i4]Nir Friedman, Joseph Y. Halpern:
Belief Revision: A Critique. CoRR cs.AI/0103020 (2001) - 2000
- [j7]Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini:
Tissue Classification with Gene Expression Profiles. J. Comput. Biol. 7(3-4): 559-583 (2000) - [j6]Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er:
Using Bayesian Networks to Analyze Expression Data. J. Comput. Biol. 7(3-4): 601-620 (2000) - [j5]Nir Friedman, Joseph Y. Halpern, Daphne Koller:
First-order conditional logic for default reasoning revisited. ACM Trans. Comput. Log. 1(2): 175-207 (2000) - [c41]Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller:
Discovering Hidden Variables: A Structure-Based Approach. NIPS 2000: 479-485 - [c40]Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini:
Tissue classification with gene expression profiles. RECOMB 2000: 54-64 - [c39]Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er:
Using Bayesian networks to analyze expression data. RECOMB 2000: 127-135 - [c38]Nir Friedman, Dan Geiger, Noam Lotner:
Likelihood Computations Using Value Abstraction. UAI 2000: 192-200 - [c37]Nir Friedman, Daphne Koller:
Being Bayesian about Network Structure. UAI 2000: 201-210 - [c36]Nir Friedman, Iftach Nachman:
Gaussian Process Networks. UAI 2000: 211-219
1990 – 1999
- 1999
- [j4]Nir Friedman, Joseph Y. Halpern:
Modeling Belief in Dynamic Systems, Part II: Revision and Update. J. Artif. Intell. Res. 10: 117-167 (1999) - [j3]Nir Friedman, Joseph Y. Halpern:
Belief Revision: A Critique. J. Log. Lang. Inf. 8(4): 401-420 (1999) - [c35]Nir Friedman, Lise Getoor:
Efficient learning using constrained sufficient statistics. AISTATS 1999 - [c34]Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner:
On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks. AISTATS 1999 - [c33]Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer:
Learning Probabilistic Relational Models. IJCAI 1999: 1300-1309 - [c32]Joseph Y. Halpern, Nir Friedman:
Plausibility Measures and Default Reasoning: An Overview. LICS 1999: 130-135 - [c31]Xavier Boyen, Nir Friedman, Daphne Koller:
Discovering the Hidden Structure of Complex Dynamic Systems. UAI 1999: 91-100 - [c30]Richard Dearden, Nir Friedman, David Andre:
Model based Bayesian Exploration. UAI 1999: 150-159 - [c29]Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner:
Data Analysis with Bayesian Networks: A Bootstrap Approach. UAI 1999: 196-205 - [c28]Nir Friedman, Iftach Nachman, Dana Pe'er:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. UAI 1999: 206-215 - [i3]Nir Friedman, Joseph Y. Halpern:
Modeling Belief in Dynamic Systems, Part II: Revision and Update. CoRR cs.AI/9903016 (1999) - 1998
- [c27]Craig Boutilier, Nir Friedman, Joseph Y. Halpern:
Belief Revision with Unreliable Observations. AAAI/IAAI 1998: 127-134 - [c26]Nir Friedman, Daphne Koller, Avi Pfeffer:
Structured Representation of Complex Stochastic Systems. AAAI/IAAI 1998: 157-164 - [c25]Richard Dearden, Nir Friedman, Stuart Russell:
Bayesian Q-Learning. AAAI/IAAI 1998: 761-768 - [c24]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 - [c23]Nir Friedman, Yoram Singer:
Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS 1998: 417-423