Nir Friedman

According to our database, Nir Friedman authored at least 107 papers between 1994 and 2019.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2019

Gene expression cartography.

Mor Nitzan, Nikos Karaiskos, Nir Friedman, Nikolaus Rajewsky

Nat., 2019

2017

McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences.

Nili Tickotsky, Tal Sagiv, Jaime Prilusky, Eric Shifrut, Nir Friedman

Bioinform., 2017

2014

Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence.

Niclas Thomas, Katharine Best, Mattia Cinelli, Shlomit Reich-Zeliger, Hilah Gal, Eric Shifrut, Asaf Madi, Nir Friedman, John Shawe-Taylor, Benny Chain

Bioinform., 2014

2013

Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)

Adnan Darwiche, Nir Friedman

CoRR, 2013

Likelihood Computations Using Value Abstractions

Nir Friedman, Dan Geiger, Noam Lotner

CoRR, 2013

Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables

Tal El-Hay, Nir Friedman

CoRR, 2013

2012

Balancing speed and accuracy of polyclonal T cell activation: a role for extracellular feedback.

Yonatan Savir, Nir Waysbort, Yaron E. Antebi, Tsvi Tlusty, Nir Friedman

BMC Syst. Biol., 2012

2011

An integrative clustering and modeling algorithm for dynamical gene expression data.

Julia Sivriver, Naomi Habib, Nir Friedman

Bioinform., 2011

Physical Module Networks: an integrative approach for reconstructing transcription regulation.

Noa Novershtern, Aviv Regev, Nir Friedman

Bioinform., 2011

2010

Mean Field Variational Approximation for Continuous-Time Bayesian Networks.

Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman

J. Mach. Learn. Res., 2010

Modularity and directionality in genetic interaction maps.

Ariel Jaimovich, Ruty Rinott, Maya Schuldiner, Hanah Margalit, Nir Friedman

Bioinform., 2010

Continuous-Time Belief Propagation.

Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Identifying novel constrained elements by exploiting biased substitution patterns.

Manuel Garber, Mitchell Guttman, Michele E. Clamp, Michael C. Zody, Nir Friedman, Xiaohui Xie

Bioinform., 2009

Convexifying the Bethe Free Energy.

Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman

Proceedings of the UAI 2009, 2009

Probabilistic Graphical Models - Principles and Techniques.

Daphne Koller, Nir Friedman

MIT Press, ISBN: 978-0-262-01319-2, 2009

2008

Optimal video stream multiplexing through linear programming.

Helman I. Stern, Ofer Hadar, Nir Friedman

Signal Process. Image Commun., 2008

A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval.

Naomi Habib, Tommy Kaplan, Hanah Margalit, Nir Friedman

PLoS Comput. Biol., 2008

Gibbs Sampling in Factorized Continuous-Time Markov Processes.

Tal El-Hay, Nir Friedman, Raz Kupferman

Proceedings of the UAI 2008, 2008

Nucleosome positioning from tiling microarray data.

Moran Yassour, Tommy Kaplan, Ariel Jaimovich, Nir Friedman

Proceedings of the Proceedings 16th International Conference on Intelligent Systems for Molecular Biology (ISMB), 2008

2007

"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks.

Gal Elidan, Iftach Nachman, Nir Friedman

J. Mach. Learn. Res., 2007

Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates.

Matan Ninio, Eyal Privman, Tal Pupko, Nir Friedman

Bioinform., 2007

Template Based Inference in Symmetric Relational Markov Random Fields.

Ariel Jaimovich, Ofer Meshi, Nir Friedman

Proceedings of the UAI 2007, 2007

Automatic genome-wide reconstruction of phylogenetic gene trees.

Ilan Wapinski, Avi Pfeffer, Nir Friedman, Aviv Regev

Proceedings of the Proceedings 15th International Conference on Intelligent Systems for Molecular Biology (ISMB) & 6th European Conference on Computational Biology (ECCB), 2007

2006

Multivariate Information Bottleneck.

Noam Slonim, Nir Friedman, Naftali Tishby

Neural Comput., 2006

Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach.

Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman

J. Comput. Biol., 2006

Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks.

Nir Friedman, Raz Kupferman

Proceedings of the UAI '06, 2006

Continuous Time Markov Networks.

Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman

Proceedings of the UAI '06, 2006

2005

Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge.

Tommy Kaplan, Nir Friedman, Hanah Margalit

PLoS Comput. Biol., 2005

Learning Module Networks.

Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman

J. Mach. Learn. Res., 2005

Learning Hidden Variable Networks: The Information Bottleneck Approach.

Gal Elidan, Nir Friedman

J. Mach. Learn. Res., 2005

CIS: compound importance sampling method for protein-DNA binding site p-value estimation.

Yoseph Barash, Gal Elidan, Tommy Kaplan, Nir Friedman

Bioinform., 2005

Predicting Transcription Factor Binding Sites Using Structural Knowledge.

Tommy Kaplan, Nir Friedman, Hanah Margalit

Proceedings of the Research in Computational Molecular Biology, 2005

Towards an Integrated Protein-Protein Interaction Network.

Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman

Proceedings of the Research in Computational Molecular Biology, 2005

A Gamma mixture model better accounts for among site rate heterogeneity.

Itay Mayrose, Nir Friedman, Tal Pupko

Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005

2004

Efficient Exact p-Value Computation for Small Sample, Sparse, and Surprising Categorical Data.

Gill Bejerano, Nir Friedman, Naftali Tishby

J. Comput. Biol., 2004

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays.

Yoseph Barash, Elinor Dehan, Meir Krupsky, Wilbur Franklin, Marc Geraci, Nir Friedman, Naftali Kaminski

Bioinform., 2004

"Ideal Parent" Structure Learning for Continuous Variable Networks.

Iftach Nachman, Gal Elidan, Nir Friedman

Proceedings of the UAI '04, 2004

Inferring quantitative models of regulatory networks from expression data.

Iftach Nachman, Aviv Regev, Nir Friedman

Proceedings of the Proceedings Twelfth International Conference on Intelligent Systems for Molecular Biology/Third European Conference on Computational Biology 2004, 2004

2003

Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks.

Nir Friedman, Daphne Koller

Mach. Learn., 2003

Modeling Belief in Dynamic Systems, Part II: Revisions and Update

Nir Friedman, Joseph Y. Halpern

CoRR, 2003

The Information Bottleneck EM Algorithm.

Gal Elidan, Nir Friedman

Proceedings of the UAI '03, 2003

Modeling dependencies in protein-DNA binding sites.

Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan

Proceedings of the Sventh Annual International Conference on Computational Biology, 2003

Probabilistic models for identifying regulation networks.

Nir Friedman

Proceedings of the European Conference on Computational Biology (ECCB 2003), 2003

2002

Learning Probabilistic Models of Link Structure.

Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar

J. Mach. Learn. Res., 2002

A Structural EM Algorithm for Phylogenetic Inference.

Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko

J. Comput. Biol., 2002

Context-Specific Bayesian Clustering for Gene Expression Data.

Yoseph Barash, Nir Friedman

J. Comput. Biol., 2002

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.

Tal Pupko, Itsik Pe'er, Masami Hasegawa, Dan Graur, Nir Friedman

Bioinform., 2002

Unsupervised document classification using sequential information maximization.

Noam Slonim, Nir Friedman, Naftali Tishby

Proceedings of the SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

Robust temporal and spectral modeling for query By melody.

Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer

Proceedings of the SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

From promoter sequence to expression: a probabilistic framework.

Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller

Proceedings of the Sixth Annual International Conference on Computational Biology, 2002

Data Perturbation for Escaping Local Maxima in Learning.

Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans

Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28, 2002

2001

Plausibility measures and default reasoning.

Nir Friedman, Joseph Y. Halpern

J. ACM, 2001

On decision-theoretic foundations for defaults.

Ronen I. Brafman, Nir Friedman

Artif. Intell., 2001

A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites.

Yoseph Barash, Gill Bejerano, Nir Friedman

Proceedings of the Algorithms in Bioinformatics, First International Workshop, 2001

Multivariate Information Bottleneck.

Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Learning the Dimensionality of Hidden Variables.

Gal Elidan, Nir Friedman

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables.

Tal El-Hay, Nir Friedman

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Class discovery in gene expression data.

Amir Ben-Dor, Nir Friedman, Zohar Yakhini

Proceedings of the Fifth Annual International Conference on Computational Biology, 2001

Agglomerative Multivariate Information Bottleneck.

Noam Slonim, Nir Friedman, Naftali Tishby

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Rich probabilistic models for gene expression.

Eran Segal, Benjamin Taskar, Audrey P. Gasch, Nir Friedman, Daphne Koller

Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001

Inferring subnetworks from perturbed expression profiles.

Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman

Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001

Learning Probabilistic Models of Relational Structure.

Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

2000

First-order conditional logic for default reasoning revisited.

Nir Friedman, Joseph Y. Halpern, Daphne Koller

ACM Trans. Comput. Log., 2000

Using Bayesian Networks to Analyze Expression Data.

Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er

J. Comput. Biol., 2000

Tissue Classification with Gene Expression Profiles.

Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini

J. Comput. Biol., 2000

Gaussian Process Networks.

Nir Friedman, Iftach Nachman

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Being Bayesian about Network Structure.

Nir Friedman, Daphne Koller

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Likelihood Computations Using Value Abstraction.

Nir Friedman, Dan Geiger, Noam Lotner

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Discovering Hidden Variables: A Structure-Based Approach.

Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

Belief Revision: A Critique.

Nir Friedman, Joseph Y. Halpern

J. Log. Lang. Inf., 1999

Modeling Belief in Dynamic Systems, Part II: Revision and Update.

Nir Friedman, Joseph Y. Halpern

J. Artif. Intell. Res., 1999

Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm.

Nir Friedman, Iftach Nachman, Dana Pe'er

Proceedings of the UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30, 1999

Data Analysis with Bayesian Networks: A Bootstrap Approach.

Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner

Proceedings of the UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30, 1999

Model based Bayesian Exploration.

Richard Dearden, Nir Friedman, David Andre

Proceedings of the UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30, 1999

Discovering the Hidden Structure of Complex Dynamic Systems.

Xavier Boyen, Nir Friedman, Daphne Koller

Proceedings of the UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30, 1999

Plausibility Measures and Default Reasoning: An Overview.

Joseph Y. Halpern, Nir Friedman

Proceedings of the 14th Annual IEEE Symposium on Logic in Computer Science, 1999

Learning Probabilistic Relational Models.

Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer

Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999

On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks.

Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner

Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999

Efficient learning using constrained sufficient statistics.

Nir Friedman, Lise Getoor

Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999

1998

Learning the Structure of Dynamic Probabilistic Networks.

Nir Friedman, Kevin P. Murphy, Stuart Russell

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

The Bayesian Structural EM Algorithm.

Nir Friedman

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

Efficient Bayesian Parameter Estimation in Large Discrete Domains.

Nir Friedman, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting.

Nir Friedman, Moisés Goldszmidt, Thomas J. Lee

Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), 1998

Structured Representation of Complex Stochastic Systems.

Nir Friedman, Daphne Koller, Avi Pfeffer

Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, 1998

Bayesian Q-Learning.

Richard Dearden, Nir Friedman, Stuart Russell

Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, 1998

Belief Revision with Unreliable Observations.

Craig Boutilier, Nir Friedman, Joseph Y. Halpern

Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, 1998

Learning Bayesian Networks with Local Structure.

Nir Friedman, Moisés Goldszmidt

Proceedings of the Learning in Graphical Models, 1998

1997

Modeling beliefs in dynamic systems.

Nir Friedman

PhD thesis, 1997

Bayesian Network Classifiers.

Nir Friedman, Dan Geiger, Moisés Goldszmidt

Mach. Learn., 1997

Modeling Belief in Dynamic Systems, Part I: Foundations.

Nir Friedman, Joseph Y. Halpern

Artif. Intell., 1997

Image Segmentation in Video Sequences: A Probabilistic Approach.

Nir Friedman, Stuart Russell

Proceedings of the UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 1997

Sequential Update of Bayesian Network Structure.

Nir Friedman, Moisés Goldszmidt

Proceedings of the UAI '97: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 1997

Generalized Prioritized Sweeping.

David Andre, Nir Friedman, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Challenge: What is the Impact of Bayesian Networks on Learning?

Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart Russell

Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997

Learning Belief Networks in the Presence of Missing Values and Hidden Variables.

Nir Friedman

Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), 1997

1996

On the Sample Complexity of Learning Bayesian Networks.

Nir Friedman, Zohar Yakhini

Proceedings of the UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, 1996

A Qualitative Markov Assumption and Its Implications for Belief Change.

Nir Friedman, Joseph Y. Halpern

Proceedings of the UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, 1996

Learning Bayesian Networks with Local Structure.

Nir Friedman, Moisés Goldszmidt

Proceedings of the UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, 1996

Context-Specific Independence in Bayesian Networks.

Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller

Proceedings of the UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, 1996

Discretizing Continuous Attributes While Learning Bayesian Networks.

Nir Friedman, Moisés Goldszmidt

Proceedings of the Machine Learning, 1996

First-Order Conditional Logic Revisited.

Nir Friedman, Joseph Y. Halpern, Daphne Koller

Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, 1996

Building Classifiers Using Bayesian Networks.

Nir Friedman, Moisés Goldszmidt

Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, 1996

1995

Plausibility Measures: A User's Guide.

Nir Friedman, Joseph Y. Halpern

Proceedings of the UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 1995

1994

A Knowledge-Based Framework for Belief change, Part I: Foundations.

Nir Friedman, Joseph Y. Halpern

Proceedings of the 5th Conference on Theoretical Aspects of Reasoning about Knowledge, 1994

On the Complexity of Conditional Logics.

Nir Friedman, Joseph Y. Halpern

Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR'94). Bonn, 1994

A Knowledge-Based Framework for Belief Change, Part II: Revision and Update.

Nir Friedman, Joseph Y. Halpern

Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR'94). Bonn, 1994

Conditional Logics of Belief Change.

Nir Friedman, Joseph Y. Halpern

Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31, 1994