Nir Friedman - RankMe

149 papers A* 14A 32B 10C 2Journal 71Unranked 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	VarNMF: non-negative probabilistic factorization with source variation. Bioinform. Ela Fallik, Nir Friedman
2019	J	`jnl`	Gene expression cartography. Nat. Mor Nitzan, Nikos Karaiskos, Nir Friedman, Nikolaus Rajewsky
2017	J	`jnl`	McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinform. Nili Tickotsky, Tal Sagiv, Jaime Prilusky, Eric Shifrut, Nir Friedman
2014	J	`jnl`	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. Niclas Thomas, Katharine Best, Mattia Cinelli, Shlomit Reich-Zeliger, Hilah Gal, Eric Shifrut, Asaf Madi, Nir Friedman, John Shawe-Taylor, Benny Chain
2013	J	`jnl`	A Qualitative Markov Assumption and its Implications for Belief Change CoRR Nir Friedman, Joseph Y. Halpern
2013	J	`jnl`	Being Bayesian about Network Structure CoRR Nir Friedman, Daphne Koller
2013	J	`jnl`	Context-Specific Independence in Bayesian Networks CoRR Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller
2013	J	`jnl`	Data Analysis with Bayesian Networks: A Bootstrap Approach CoRR Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner
2013	J	`jnl`	Discovering the Hidden Structure of Complex Dynamic Systems CoRR Xavier Boyen, Nir Friedman, Daphne Koller
2013	J	`jnl`	Gaussian Process Networks CoRR Nir Friedman, Iftach Nachman
2013	J	`jnl`	Image Segmentation in Video Sequences: A Probabilistic Approach CoRR Nir Friedman, Stuart Russell
2013	J	`jnl`	Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables CoRR Tal El-Hay, Nir Friedman
2013	J	`jnl`	Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm CoRR Nir Friedman, Iftach Nachman, Dana Pe'er
2013	J	`jnl`	Learning Bayesian Networks with Local Structure CoRR Nir Friedman, Moisés Goldszmidt
2013	J	`jnl`	Learning the Dimensionality of Hidden Variables CoRR Gal Elidan, Nir Friedman
2013	J	`jnl`	Learning the Structure of Dynamic Probabilistic Networks CoRR Nir Friedman, Kevin P. Murphy, Stuart Russell
2013	J	`jnl`	Likelihood Computations Using Value Abstractions CoRR Nir Friedman, Dan Geiger, Noam Lotner
2013	J	`jnl`	Model-Based Bayesian Exploration CoRR Richard Dearden, Nir Friedman, David Andre
2013	J	`jnl`	Multivariate Information Bottleneck CoRR Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
2013	J	`jnl`	On the Sample Complexity of Learning Bayesian Networks CoRR Nir Friedman, Zohar Yakhini
2013	J	`jnl`	Plausibility Measures: A User's Guide CoRR Nir Friedman, Joseph Y. Halpern
2013	J	`jnl`	Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002) CoRR Adnan Darwiche, Nir Friedman
2013	J	`jnl`	Sequential Update of Bayesian Network Structure CoRR Nir Friedman, Moisés Goldszmidt
2013	J	`jnl`	The Bayesian Structural EM Algorithm CoRR Nir Friedman
2012	J	`jnl`	"Ideal Parent" Structure Learning for Continuous Variable Networks CoRR Iftach Nachman, Gal Elidan, Nir Friedman
2012	J	`jnl`	Balancing speed and accuracy of polyclonal T cell activation: a role for extracellular feedback. BMC Syst. Biol. Yonatan Savir, Nir Waysbort, Yaron E. Antebi, Tsvi Tlusty, Nir Friedman
2012	J	`jnl`	Continuous Time Markov Networks. CoRR Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman
2012	J	`jnl`	Convexifying the Bethe Free Energy CoRR Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman
2012	J	`jnl`	Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. CoRR Nir Friedman, Raz Kupferman
2012	J	`jnl`	Gibbs Sampling in Factorized Continuous-Time Markov Processes CoRR Tal El-Hay, Nir Friedman, Raz Kupferman
2012	J	`jnl`	Learning Module Networks CoRR Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman
2012	J	`jnl`	Mean Field Variational Approximation for Continuous-Time Bayesian Networks CoRR Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
2012	J	`jnl`	Template Based Inference in Symmetric Relational Markov Random Fields CoRR Ariel Jaimovich, Ofer Meshi, Nir Friedman
2012	J	`jnl`	The Information Bottleneck EM Algorithm CoRR Gal Elidan, Nir Friedman
2011	J	`jnl`	An integrative clustering and modeling algorithm for dynamical gene expression data. Bioinform. Julia Sivriver, Naomi Habib, Nir Friedman
2011	J	`jnl`	Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinform. Noa Novershtern, Aviv Regev, Nir Friedman
2010	A*	`conf`	Continuous-Time Belief Propagation. ICML Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman
2010	J	`jnl`	Mean Field Variational Approximation for Continuous-Time Bayesian Networks. J. Mach. Learn. Res. Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
2010	J	`jnl`	Modularity and directionality in genetic interaction maps. Bioinform. Ariel Jaimovich, Ruty Rinott, Maya Schuldiner, Hanah Margalit, Nir Friedman
2009	A	`conf`	Convexifying the Bethe Free Energy. UAI Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman
2009	J	`jnl`	Identifying novel constrained elements by exploiting biased substitution patterns. Bioinform. Manuel Garber, Mitchell Guttman, Michele E. Clamp, Michael C. Zody, Nir Friedman, Xiaohui Xie
2009	A	`conf`	Mean Field Variational Approximation for Continuous-Time Bayesian Networks. UAI Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
2009	—	`book`	Probabilistic Graphical Models - Principles and Techniques. Daphne Koller, Nir Friedman
2008	J	`jnl`	A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval. PLoS Comput. Biol. Naomi Habib, Tommy Kaplan, Hanah Margalit, Nir Friedman
2008	A	`conf`	Gibbs Sampling in Factorized Continuous-Time Markov Processes. UAI Tal El-Hay, Nir Friedman, Raz Kupferman
2008	A	`conf`	Nucleosome positioning from tiling microarray data. ISMB Moran Yassour, Tommy Kaplan, Ariel Jaimovich, Nir Friedman
2008	J	`jnl`	Optimal video stream multiplexing through linear programming. Signal Process. Image Commun. Helman I. Stern, Ofer Hadar, Nir Friedman
2007	J	`jnl`	"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. J. Mach. Learn. Res. Gal Elidan, Iftach Nachman, Nir Friedman
2007	—	`conf`	Automatic genome-wide reconstruction of phylogenetic gene trees. ISMB/ECCB (Supplement of Bioinformatics) Ilan Wapinski, Avi Pfeffer, Nir Friedman, Aviv Regev
2007	J	`jnl`	Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates. Bioinform. Matan Ninio, Eyal Privman, Tal Pupko, Nir Friedman
2007	A	`conf`	Template Based Inference in Symmetric Relational Markov Random Fields. UAI Ariel Jaimovich, Ofer Meshi, Nir Friedman
2006	A	`conf`	Continuous Time Markov Networks. UAI Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman
2006	A	`conf`	Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks. UAI Nir Friedman, Raz Kupferman
2006	J	`jnl`	Multivariate Information Bottleneck. Neural Comput. Noam Slonim, Nir Friedman, Naftali Tishby
2006	J	`jnl`	Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach. J. Comput. Biol. Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman
2005	—	`conf`	A Gamma mixture model better accounts for among site rate heterogeneity. ECCB/JBI Itay Mayrose, Nir Friedman, Tal Pupko
2005	J	`jnl`	Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge. PLoS Comput. Biol. Tommy Kaplan, Nir Friedman, Hanah Margalit
2005	J	`jnl`	CIS: compound importance sampling method for protein-DNA binding site p-value estimation. Bioinform. Yoseph Barash, Gal Elidan, Tommy Kaplan, Nir Friedman
2005	J	`jnl`	Learning Hidden Variable Networks: The Information Bottleneck Approach. J. Mach. Learn. Res. Gal Elidan, Nir Friedman
2005	J	`jnl`	Learning Module Networks. J. Mach. Learn. Res. Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman
2005	B	`conf`	Predicting Transcription Factor Binding Sites Using Structural Knowledge. RECOMB Tommy Kaplan, Nir Friedman, Hanah Margalit
2005	B	`conf`	Towards an Integrated Protein-Protein Interaction Network. RECOMB Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman
2004	A	`conf`	"Ideal Parent" Structure Learning for Continuous Variable Networks. UAI Iftach Nachman, Gal Elidan, Nir Friedman
2004	J	`jnl`	Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinform. Yoseph Barash, Elinor Dehan, Meir Krupsky, Wilbur Franklin, Marc Geraci, Nir Friedman, Naftali Kaminski
2004	J	`jnl`	Efficient Exact p-Value Computation for Small Sample, Sparse, and Surprising Categorical Data. J. Comput. Biol. Gill Bejerano, Nir Friedman, Naftali Tishby
2004	—	`conf`	Inferring quantitative models of regulatory networks from expression data. ISMB/ECCB (Supplement of Bioinformatics) Iftach Nachman, Aviv Regev, Nir Friedman
2003	J	`jnl`	Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks. Mach. Learn. Nir Friedman, Daphne Koller
2003	A	`conf`	Learning Module Networks. UAI Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman
2003	J	`jnl`	Modeling Belief in Dynamic Systems, Part I: Foundations CoRR Nir Friedman, Joseph Y. Halpern
2003	J	`jnl`	Modeling Belief in Dynamic Systems, Part II: Revisions and Update CoRR Nir Friedman, Joseph Y. Halpern
2003	B	`conf`	Modeling dependencies in protein-DNA binding sites. RECOMB Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
2003	C	`conf`	Probabilistic models for identifying regulation networks. ECCB Nir Friedman
2003	A	`conf`	The Information Bottleneck EM Algorithm. UAI Gal Elidan, Nir Friedman
2002	J	`jnl`	A Structural EM Algorithm for Phylogenetic Inference. J. Comput. Biol. Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko
2002	J	`jnl`	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. Tal Pupko, Itsik Pe'er, Masami Hasegawa, Dan Graur, Nir Friedman
2002	J	`jnl`	Context-Specific Bayesian Clustering for Gene Expression Data. J. Comput. Biol. Yoseph Barash, Nir Friedman
2002	—	`conf`	Data Perturbation for Escaping Local Maxima in Learning. AAAI/IAAI Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans
2002	B	`conf`	From promoter sequence to expression: a probabilistic framework. RECOMB Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
2002	J	`jnl`	Learning Probabilistic Models of Link Structure. J. Mach. Learn. Res. Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
2002	A*	`conf`	Robust temporal and spectral modeling for query By melody. SIGIR Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer
2002	A	`ed.`	UAI '02, Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence, University of Alberta, Edmonton, Alberta, Canada, August 1-4, 2002 UAI Adnan Darwiche, Nir Friedman
2002	A*	`conf`	Unsupervised document classification using sequential information maximization. SIGIR Noam Slonim, Nir Friedman, Naftali Tishby
2001	C	`conf`	A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. WABI Yoseph Barash, Gill Bejerano, Nir Friedman
2001	B	`conf`	A structural EM algorithm for phylogenetic inference. RECOMB Nir Friedman, Matan Ninio, Itsik Pe'er, Tal Pupko
2001	—	`conf`	Agglomerative Multivariate Information Bottleneck. NIPS Noam Slonim, Nir Friedman, Naftali Tishby
2001	J	`jnl`	Belief Revision: A Critique CoRR Nir Friedman, Joseph Y. Halpern
2001	B	`conf`	Class discovery in gene expression data. RECOMB Amir Ben-Dor, Nir Friedman, Zohar Yakhini
2001	B	`conf`	Context-specific Bayesian clustering for gene expression data. RECOMB Yoseph Barash, Nir Friedman
2001	A	`conf`	Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables. UAI Tal El-Hay, Nir Friedman
2001	—	`conf`	Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman
2001	A*	`conf`	Learning Probabilistic Models of Relational Structure. ICML Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
2001	A	`conf`	Learning the Dimensionality of Hidden Variables. UAI Gal Elidan, Nir Friedman
2001	A	`conf`	Multivariate Information Bottleneck. UAI Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
2001	J	`jnl`	On decision-theoretic foundations for defaults. Artif. Intell. Ronen I. Brafman, Nir Friedman
2001	J	`jnl`	Plausibility measures and default reasoning. J. ACM Nir Friedman, Joseph Y. Halpern
2001	—	`conf`	Rich probabilistic models for gene expression. ISMB (Supplement of Bioinformatics) Eran Segal, Benjamin Taskar, Audrey P. Gasch, Nir Friedman, Daphne Koller
2000	A	`conf`	Being Bayesian about Network Structure. UAI Nir Friedman, Daphne Koller
2000	—	`conf`	Discovering Hidden Variables: A Structure-Based Approach. NIPS Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
2000	J	`jnl`	First-order conditional logic for default reasoning revisited. ACM Trans. Comput. Log. Nir Friedman, Joseph Y. Halpern, Daphne Koller
2000	A	`conf`	Gaussian Process Networks. UAI Nir Friedman, Iftach Nachman
2000	A	`conf`	Likelihood Computations Using Value Abstraction. UAI Nir Friedman, Dan Geiger, Noam Lotner
2000	J	`jnl`	Tissue Classification with Gene Expression Profiles. J. Comput. Biol. Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini
2000	B	`conf`	Tissue classification with gene expression profiles. RECOMB Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini
2000	J	`jnl`	Using Bayesian Networks to Analyze Expression Data. J. Comput. Biol. Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er
2000	B	`conf`	Using Bayesian networks to analyze expression data. RECOMB Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er
1999	J	`jnl`	Belief Revision: A Critique. J. Log. Lang. Inf. Nir Friedman, Joseph Y. Halpern
1999	A	`conf`	Data Analysis with Bayesian Networks: A Bootstrap Approach. UAI Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner
1999	A	`conf`	Discovering the Hidden Structure of Complex Dynamic Systems. UAI Xavier Boyen, Nir Friedman, Daphne Koller
1999	A	`conf`	Efficient learning using constrained sufficient statistics. AISTATS Nir Friedman, Lise Getoor
1999	A	`conf`	Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. UAI Nir Friedman, Iftach Nachman, Dana Pe'er
1999	A*	`conf`	Learning Probabilistic Relational Models. IJCAI Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
1999	A	`conf`	Model based Bayesian Exploration. UAI Richard Dearden, Nir Friedman, David Andre
1999	J	`jnl`	Modeling Belief in Dynamic Systems, Part II: Revision and Update CoRR Nir Friedman, Joseph Y. Halpern
1999	J	`jnl`	Modeling Belief in Dynamic Systems, Part II: Revision and Update. J. Artif. Intell. Res. Nir Friedman, Joseph Y. Halpern
1999	A	`conf`	On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks. AISTATS Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner
1999	A*	`conf`	Plausibility Measures and Default Reasoning: An Overview. LICS Joseph Y. Halpern, Nir Friedman
1998	A*	`conf`	Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting. ICML Nir Friedman, Moisés Goldszmidt, Thomas J. Lee
1998	—	`conf`	Bayesian Q-Learning. AAAI/IAAI Richard Dearden, Nir Friedman, Stuart Russell
1998	—	`conf`	Belief Revision with Unreliable Observations. AAAI/IAAI Craig Boutilier, Nir Friedman, Joseph Y. Halpern
1998	—	`conf`	Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS Nir Friedman, Yoram Singer
1998	J	`jnl`	First-Order Conditional Logic Revisited CoRR Nir Friedman, Joseph Y. Halpern, Daphne Koller
1998	—	`ch.`	Learning Bayesian Networks with Local Structure. Learning in Graphical Models Nir Friedman, Moisés Goldszmidt
1998	A	`conf`	Learning the Structure of Dynamic Probabilistic Networks. UAI Nir Friedman, Kevin P. Murphy, Stuart Russell
1998	J	`jnl`	Plausibility Measures and Default Reasoning CoRR Nir Friedman, Joseph Y. Halpern
1998	—	`conf`	Structured Representation of Complex Stochastic Systems. AAAI/IAAI Nir Friedman, Daphne Koller, Avi Pfeffer
1998	A	`conf`	The Bayesian Structural EM Algorithm. UAI Nir Friedman
1997	J	`jnl`	Bayesian Network Classifiers. Mach. Learn. Nir Friedman, Dan Geiger, Moisés Goldszmidt
1997	—	`conf`	Challenge: What is the Impact of Bayesian Networks on Learning? IJCAI (1) Nir Friedman, Moisés Goldszmidt, David Heckerman, Stuart Russell
1997	—	`conf`	Generalized Prioritized Sweeping. NIPS David Andre, Nir Friedman, Ronald Parr
1997	A	`conf`	Image Segmentation in Video Sequences: A Probabilistic Approach. UAI Nir Friedman, Stuart Russell
1997	A*	`conf`	Learning Belief Networks in the Presence of Missing Values and Hidden Variables. ICML Nir Friedman
1997	J	`jnl`	Modeling Belief in Dynamic Systems, Part I: Foundations. Artif. Intell. Nir Friedman, Joseph Y. Halpern
1997	—	`—`	Modeling beliefs in dynamic systems. Nir Friedman
1997	A	`conf`	Sequential Update of Bayesian Network Structure. UAI Nir Friedman, Moisés Goldszmidt
1996	A	`conf`	A Qualitative Markov Assumption and Its Implications for Belief Change. UAI Nir Friedman, Joseph Y. Halpern
1996	A*	`conf`	Belief Revision: A Critique. KR Nir Friedman, Joseph Y. Halpern
1996	—	`conf`	Building Classifiers Using Bayesian Networks. AAAI/IAAI, Vol. 2 Nir Friedman, Moisés Goldszmidt
1996	A	`conf`	Context-Specific Independence in Bayesian Networks. UAI Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller
1996	A*	`conf`	Discretizing Continuous Attributes While Learning Bayesian Networks. ICML Nir Friedman, Moisés Goldszmidt
1996	—	`conf`	First-Order Conditional Logic Revisited. AAAI/IAAI, Vol. 2 Nir Friedman, Joseph Y. Halpern, Daphne Koller
1996	A	`conf`	Learning Bayesian Networks with Local Structure. UAI Nir Friedman, Moisés Goldszmidt
1996	A	`conf`	On the Sample Complexity of Learning Bayesian Networks. UAI Nir Friedman, Zohar Yakhini
1996	—	`conf`	Plausibility Measures and Default Reasoning. AAAI/IAAI, Vol. 2 Nir Friedman, Joseph Y. Halpern
1995	A*	`conf`	On Decision-Theoretic Foundations for Defaults. IJCAI Ronen I. Brafman, Nir Friedman
1995	A	`conf`	Plausibility Measures: A User's Guide. UAI Nir Friedman, Joseph Y. Halpern
1994	A*	`conf`	A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. KR Nir Friedman, Joseph Y. Halpern
1994	B	`conf`	A Knowledge-Based Framework for Belief change, Part I: Foundations. TARK Nir Friedman, Joseph Y. Halpern
1994	A*	`conf`	Conditional Logics of Belief Change. AAAI Nir Friedman, Joseph Y. Halpern
1994	A*	`conf`	On the Complexity of Conditional Logics. KR Nir Friedman, Joseph Y. Halpern