Conditional Logics of Belief Change. - Nir Friedman; Joseph Y. Halpern (AAAI, 1994)

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

On the Complexity of Conditional Logics. - Nir Friedman; Joseph Y. Halpern (KR, 1994)

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

Plausibility Measures: A User's Guide. - Nir Friedman; Joseph Y. Halpern (UAI, 1995)

Building Classifiers Using Bayesian Networks. - Nir Friedman; Moisés Goldszmidt (AAAI/IAAI, Vol. 2, 1996)

First-Order Conditional Logic Revisited. - Nir Friedman; Joseph Y. Halpern; Daphne Koller (AAAI/IAAI, Vol. 2, 1996)

Discretizing Continuous Attributes While Learning Bayesian Networks. - Nir Friedman; Moisés Goldszmidt (ICML, 1996)

Context-Specific Independence in Bayesian Networks. - Craig Boutilier; Nir Friedman; Moisés Goldszmidt; Daphne Koller (UAI, 1996)

Learning Bayesian Networks with Local Structure. - Nir Friedman; Moisés Goldszmidt (UAI, 1996)

A Qualitative Markov Assumption and Its Implications for Belief Change. - Nir Friedman; Joseph Y. Halpern (UAI, 1996)

On the Sample Complexity of Learning Bayesian Networks. - Nir Friedman; Zohar Yakhini (UAI, 1996)

Learning Belief Networks in the Presence of Missing Values and Hidden Variables. - Nir Friedman (ICML, 1997)

Challenge: What is the Impact of Bayesian Networks on Learning? - Nir Friedman; Moisés Goldszmidt; David Heckerman; Stuart Russell 001 (IJCAI (1), 1997)

Generalized Prioritized Sweeping. - David Andre; Nir Friedman; Ronald Parr (NIPS, 1997)

Sequential Update of Bayesian Network Structure. - Nir Friedman; Moisés Goldszmidt (UAI, 1997)

Image Segmentation in Video Sequences: A Probabilistic Approach. - Nir Friedman; Stuart Russell 001 (UAI, 1997)

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

Bayesian Network Classifiers. - Nir Friedman; Dan Geiger; Moisés Goldszmidt (Mach. Learn., 1997)

Modeling beliefs in dynamic systems. - Nir Friedman (1997)

Learning Bayesian Networks with Local Structure. - Nir Friedman; Moisés Goldszmidt (Learning in Graphical Models, 1998)

Belief Revision with Unreliable Observations. - Craig Boutilier; Nir Friedman; Joseph Y. Halpern (AAAI/IAAI, 1998)

Bayesian Q-Learning. - Richard Dearden; Nir Friedman; Stuart Russell 001 (AAAI/IAAI, 1998)

Structured Representation of Complex Stochastic Systems. - Nir Friedman; Daphne Koller; Avi Pfeffer (AAAI/IAAI, 1998)

Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting. - Nir Friedman; Moisés Goldszmidt; Thomas J. Lee (ICML, 1998)

Efficient Bayesian Parameter Estimation in Large Discrete Domains. - Nir Friedman; Yoram Singer (NIPS, 1998)

The Bayesian Structural EM Algorithm. - Nir Friedman (UAI, 1998)

Learning the Structure of Dynamic Probabilistic Networks. - Nir Friedman; Kevin P. Murphy; Stuart Russell 001 (UAI, 1998)

Efficient learning using constrained sufficient statistics. - Nir Friedman; Lise Getoor (AISTATS, 1999)

On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks. - Nir Friedman; Moisés Goldszmidt; Abraham J. Wyner (AISTATS, 1999)

Learning Probabilistic Relational Models. - Nir Friedman; Lise Getoor; Daphne Koller; Avi Pfeffer (IJCAI, 1999)

Plausibility Measures and Default Reasoning: An Overview. - Joseph Y. Halpern; Nir Friedman (LICS, 1999)

Discovering the Hidden Structure of Complex Dynamic Systems. - Xavier Boyen; Nir Friedman; Daphne Koller (UAI, 1999)

Model based Bayesian Exploration. - Richard Dearden; Nir Friedman; David Andre (UAI, 1999)

Data Analysis with Bayesian Networks: A Bootstrap Approach. - Nir Friedman; Moisés Goldszmidt; Abraham J. Wyner (UAI, 1999)

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

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

Belief Revision: A Critique. - Nir Friedman; Joseph Y. Halpern (J. Log. Lang. Inf., 1999)

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

Likelihood Computations Using Value Abstraction. - Nir Friedman; Dan Geiger; Noam Lotner (UAI, 2000)

Being Bayesian about Network Structure. - Nir Friedman; Daphne Koller (UAI, 2000)

Gaussian Process Networks. - Nir Friedman; Iftach Nachman (UAI, 2000)

Tissue Classification with Gene Expression Profiles. - Amir Ben-Dor; Laurakay Bruhn; Nir Friedman; Iftach Nachman; Michèl Schummer; Zohar Yakhini (J. Comput. Biol., 2000)

Using Bayesian Networks to Analyze Expression Data. - Nir Friedman; Michal Linial; Iftach Nachman; Dana Pe'er (J. Comput. Biol., 2000)

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

Learning Probabilistic Models of Relational Structure. - Lise Getoor; Nir Friedman; Daphne Koller; Benjamin Taskar (ICML, 2001)

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

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

Agglomerative Multivariate Information Bottleneck. - Noam Slonim; Nir Friedman; Naftali Tishby (NIPS, 2001)

Class discovery in gene expression data. - Amir Ben-Dor; Nir Friedman; Zohar Yakhini (RECOMB, 2001)

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

Learning the Dimensionality of Hidden Variables. - Gal Elidan; Nir Friedman (UAI, 2001)

Multivariate Information Bottleneck. - Nir Friedman; Ori Mosenzon; Noam Slonim; Naftali Tishby (UAI, 2001)

A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. - Yoseph Barash; Gill Bejerano; Nir Friedman (WABI, 2001)

On decision-theoretic foundations for defaults. - Ronen I. Brafman; Nir Friedman (Artif. Intell., 2001)

Plausibility measures and default reasoning. - Nir Friedman; Joseph Y. Halpern (J. ACM, 2001)

Data Perturbation for Escaping Local Maxima in Learning. - Gal Elidan; Matan Ninio; Nir Friedman; Dale Schuurmans (AAAI/IAAI, 2002)

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

Robust temporal and spectral modeling for query By melody. - Shai Shalev-Shwartz; Shlomo Dubnov; Nir Friedman; Yoram Singer (SIGIR, 2002)

Unsupervised document classification using sequential information maximization. - Noam Slonim; Nir Friedman; Naftali Tishby (SIGIR, 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)

Context-Specific Bayesian Clustering for Gene Expression Data. - Yoseph Barash; Nir Friedman (J. Comput. Biol., 2002)

A Structural EM Algorithm for Phylogenetic Inference. - Nir Friedman; Matan Ninio; Itsik Pe'er; Tal Pupko (J. Comput. Biol., 2002)

Learning Probabilistic Models of Link Structure. - Lise Getoor; Nir Friedman; Daphne Koller; Benjamin Taskar (J. Mach. Learn. Res., 2002)

Probabilistic models for identifying regulation networks. - Nir Friedman (ECCB, 2003)

Modeling dependencies in protein-DNA binding sites. - Yoseph Barash; Gal Elidan; Nir Friedman; Tommy Kaplan (RECOMB, 2003)

The Information Bottleneck EM Algorithm. - Gal Elidan; Nir Friedman (UAI, 2003)

Modeling Belief in Dynamic Systems, Part II: Revisions and Update - Nir Friedman; Joseph Y. Halpern (CoRR, 2003)

Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks. - Nir Friedman; Daphne Koller (Mach. Learn., 2003)

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

"Ideal Parent" Structure Learning for Continuous Variable Networks. - Iftach Nachman; Gal Elidan; Nir Friedman (UAI, 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)

Efficient Exact p-Value Computation for Small Sample, Sparse, and Surprising Categorical Data. - Gill Bejerano; Nir Friedman; Naftali Tishby (J. Comput. Biol., 2004)

A Gamma mixture model better accounts for among site rate heterogeneity. - Itay Mayrose; Nir Friedman; Tal Pupko (ECCB/JBI, 2005)

Towards an Integrated Protein-Protein Interaction Network. - Ariel Jaimovich; Gal Elidan; Hanah Margalit; Nir Friedman (RECOMB, 2005)

Predicting Transcription Factor Binding Sites Using Structural Knowledge. - Tommy Kaplan; Nir Friedman; Hanah Margalit (RECOMB, 2005)

CIS: compound importance sampling method for protein-DNA binding site p-value estimation. - Yoseph Barash; Gal Elidan; Tommy Kaplan; Nir Friedman (Bioinform., 2005)

Learning Hidden Variable Networks: The Information Bottleneck Approach. - Gal Elidan; Nir Friedman (J. Mach. Learn. Res., 2005)

Learning Module Networks. - Eran Segal; Dana Pe'er; Aviv Regev; Daphne Koller; Nir Friedman (J. Mach. Learn. Res., 2005)

Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge. - Tommy Kaplan; Nir Friedman; Hanah Margalit (PLoS Comput. Biol., 2005)

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

Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. - Nir Friedman; Raz Kupferman (UAI, 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)

Multivariate Information Bottleneck. - Noam Slonim; Nir Friedman; Naftali Tishby (Neural Comput., 2006)

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

Template Based Inference in Symmetric Relational Markov Random Fields. - Ariel Jaimovich; Ofer Meshi; Nir Friedman (UAI, 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)

"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. - Gal Elidan; Iftach Nachman; Nir Friedman (J. Mach. Learn. Res., 2007)

Nucleosome positioning from tiling microarray data. - Moran Yassour; Tommy Kaplan; Ariel Jaimovich; Nir Friedman (ISMB, 2008)

Gibbs Sampling in Factorized Continuous-Time Markov Processes. - Tal El-Hay; Nir Friedman; Raz Kupferman (UAI, 2008)

A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval. - Naomi Habib; Tommy Kaplan; Hanah Margalit; Nir Friedman (PLoS Comput. Biol., 2008)

Optimal video stream multiplexing through linear programming. - Helman I. Stern; Ofer Hadar; Nir Friedman (Signal Process. Image Commun., 2008)

Probabilistic Graphical Models - Principles and Techniques. - Daphne Koller; Nir Friedman (MIT Press, 2009)

Convexifying the Bethe Free Energy. - Ofer Meshi; Ariel Jaimovich; Amir Globerson; Nir Friedman (UAI, 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)

Continuous-Time Belief Propagation. - Tal El-Hay; Ido Cohn; Nir Friedman; Raz Kupferman (ICML, 2010)

Modularity and directionality in genetic interaction maps. - Ariel Jaimovich; Ruty Rinott; Maya Schuldiner; Hanah Margalit; Nir Friedman (Bioinform., 2010)

Mean Field Variational Approximation for Continuous-Time Bayesian Networks. - Ido Cohn; Tal El-Hay; Nir Friedman; Raz Kupferman (J. Mach. Learn. Res., 2010)

Physical Module Networks: an integrative approach for reconstructing transcription regulation. - Noa Novershtern; Aviv Regev; Nir Friedman (Bioinform., 2011)

An integrative clustering and modeling algorithm for dynamical gene expression data. - Julia Sivriver; Naomi Habib; Nir Friedman (Bioinform., 2011)

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)

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

Likelihood Computations Using Value Abstractions - Nir Friedman; Dan Geiger; Noam Lotner (CoRR, 2013)

Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002) - Adnan Darwiche; Nir Friedman (CoRR, 2013)

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)

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)

Gene expression cartography. - Mor Nitzan; Nikos Karaiskos; Nir Friedman; Nikolaus Rajewsky (Nat., 2019)