Nir Friedman: Publications

• Refereed Journals
• Book Chapters
• Refereed Conferences
• Unpublished Works
• Others

Refereed Journals

• Context-Specific Bayesian Clustering for Gene Expression Data with Y. Barash.

To appear in Journal of Computational Biology, 2002. PostScript, PDF.
 
• A Structural EM Algorithm for Phylogentic Inference with M. Ninio, I. Pe'er, and T. Pupko.

To appear in Journal of Computational Biology, 2001. PostScript, PDF.
 
• Being Bayesian about Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks with D. Koller.

To appear in Machine Learning. PostScript, PDF.
 

• On decision-theoretic foundations for defaults with R. I. Brafman.
  Artificial Intelligence, 133:1-33, 2001.
   
• Plausibility measures and default reasoning with J. Y. Halpern.

Journal of the ACM, 48:4, 2001, pp. 648--685.   PostScript, PDF.
 

• Tissue Classification with Gene Expression Profiles with A. Ben-Dor, L. Bruhn, I. Nachman, M. Schummer, and Z. Yakhini.

Journal of Computational Biology, 7:559--584, 2000. PostScript, PDF.
 
• Using Bayesian Networks to Analyze Expression Data with M. Linial, I. Nachman, and D. Pe'er.

Journal of Computational Biology, 7:601--620, 2000. PostScript, PDF.
 
• Conditional first-order logic revisited with J. Y. Halpern, and D. Koller.

ACM Trans. Computational Logic, 1:175-207, 2000. PostScript, PDF.
 

• Modeling Beliefs in Dynamic Systems. Part II: Revision and Update with J. Y. Halpern.

In Journal of AI Research 10:117-167, 1999.  PostScript, PDF.
 
• Belief revision: A critique with J. Y. Halpern.

In Journal of Logic, Language, and Information 8:401-420,1999. PostScript, PDF.

• Bayesian networks classifiers with D. Geiger, and M. Goldszmidt.

In Machine Learning 29:131--163, 1997. PostScript, PDF.
 
• Modeling beliefs in dynamic systems Part I: Foundations with J. Y. Halpern.

In Artificial Intelligence, 95:257--316, 1997. PostScript, PDF.

Book Chapters

• Learning Bayesian Networks with Local Structure with  M. Goldszmidt.

In M. I. Jordan ed. Learning and Inference in Graphical Models. 1998. PostScript, PDF.

Refereed Conferences

• Context-Specific Bayesian Clustering for Gene Expression Data with Y. Barash.

In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2001. PostScript, PDF.
 
• A simple hyper-geometric approach for discovering putative transcription factor binding sites with Y. Barash and G. Bejerano.

In Algorithms in Bioinformatics: Proc. First International Workshop, 2001. PostScript, PDF.
 
• Class Discovery in Gene Expression Data with A. Ben-Dor, and Z. Yakhini.

In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2001. PostScript, PDF.
 
• Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables with T. El-Hay.

In Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001. PostScript, PDF.
 
• Learning the Dimensionality of Hidden Variables with G. Elidan.

In Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001. PostScript, PDF.
 
• Multivariate Information Bottleneck with Ori Mosenzon, Noam Slonim, and Tali Tishby.

In Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001. PostScript, PDF.
Recipient of the Best Paper Award.
 
• Agglomerative Multivariate Information Bottleneck with Noam Slonim, and Tali Tishby.

In Neural Information Processing Systems (NIPS 01), 2001. PostScript, PDF.
 
• A Structural EM Algorithm for Phylogentic Inference with M. Ninio, I. Pe'er, and T. Pupko.

In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2001. PostScript, PDF.
 
• Learning Probabilistic Models of Relational Structure with Lise Getoor, Ben Taskar, and Daphne Koller.

In Eighteenth International Conference on Machine Learning, 2001. PostScript, PDF.
 
• Rich Probabilistic Models for Gene Expression with E. Segal, B. Taskar, A. Gasch, and D. Koller.

In 9th Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), 2001. PostScript, PDF. Web Supplement
 
• Inferring Subnetworks from Perturbed Expression Profiles with D. Pe'er, A. Regev, and G. Elidan.

In 9th Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), 2001. PostScript, PDF. Web Supplement
Recipient of the Best Paper Award.

• Tissue Classification with Gene Expression Profiles with A. Ben-Dor, L. Bruhn, I. Nachman, M. Schummer, and Z. Yakhini.

In Proc. Fourth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2000. PostScript, PDF.
 
• Discovering Hidden Variables: A Structure-Based Approach with G. Elidan, N. Lotner, and D. Koller.

NIPS, 2000. PostScript, PDF.
 
• Being Bayesian about Network Structure with D. Koller.

In Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2000. PostScript, PDF.
 
• Likelihood Computation with Value Abstraction with D. Geiger, and N. Lotner.

In Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2000. PostScript, PDF.
 
• Using Bayesian Networks to Analyze Expression Data with M. Linial, I. Nachman, and D. Pe'er.

In Proc. Fourth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2000. PostScript, PDF.
 
• Gaussian Process Networks with I. Nachman.

In Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2000. PostScript, PDF.
 

• Discovering the Hidden Structure of Complex Dynamic Systems with X. Boyen and D. Koller.

In Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999. PostScript, PDF.
 
• Model-based Bayesian Exploration with  R. Dearden and D. Andre.

In Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999. PostScript, PDF.
 
• Learning Bayesian Network Structure from Massive Datasets: The ``Sparse Candidate'' Algorithm with I. Nachman and D. Pe'er.

In Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999. PostScript, PDF.
 
• Efficient Learning using Constrained Sufficient Statistics with L. Getoor.

In Proc. Seventh International Workshop on Artificial Intelligence and Statistics, 1999. PostScript, PDF.
 
• Learning Probabilistic Relational Models with L. Getoor, D. Koller and A. Pfeffer.

In Sixteenth International Conf. on Artificial Intelligence (IJCAI), 1999. PostScript, PDF.
 
• On the Application of The Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks with M. Goldszmidt and A. Wyner.

In Proc. Seventh International Workshop on Artificial Intelligence and Statistics, 1999. PostScript, PDF.
 
• Data analysis with Bayesian networks: a bootstrap approach with M. Goldszmidt and A. Wyner.

In Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999. PostScript, PDF.

1998

• Belief revision with unreliable observations with C. Boutilier and J. Y. Halpern.

In Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998. PostScript, PDF.
 
• Bayesian Q-Learning with R. Dearden and S. Russell.

In Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998. PostScript, PDF.
 
• The Bayesian structural EM algorithm.

In Fourteenth Conf. on Uncertainty in Artificial Intelligence (UAI). 1998. PostScript, PDF.
 
• Bayesian network classification with continuous attributes: Getting the best of both discretization and parametric fitting, with M. Goldszmidt and T. J. Lee.

In Fifteenth Inter. Conf. on Machine Learning (ICML). 1998 PostScript, PDF.
 
• Learning the structure of dynamic probabilistic networks, with K. Murphy and S. Russell.

In Fourteenth Conf. on Uncertainty in Artificial Intelligence (UAI). 1998. PostScript, PDF.
 
• Structured Representation of Complex Stochastic Systems, with D. Koller and A. Pfeffer.

In Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998. PostScript, PDF.
 
• Efficient Bayesian Parameter Estimation in Large Discrete Domains, with Y. Singer.

In Neural Information Processing Systems (NIPS 98), 1998. PostScript, PDF.

1997

• Generalized Prioritized Sweeping with D. Andre, and R. Parr

In Neural Information Processing Systems (NIPS 97), 1997.  PostScript, PDF.
 
• Learning belief networks in the presence of missing values and hidden variables..

In Fourteenth Inter. Conf. on Machine Learning (ICML). 1997.  PostScript, PDF.
 
• Challenge: Where is the Impact of Bayesian Networks in Learning? with D. Heckerman, M. Goldszmidt, and S. Russell.

In Proc. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI). 1997.  PostScript, PDF.
 
• Sequential update of Bayesian network structure with M. Goldszmidt.

In Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence (UAI). 1997. PostScript, PDF.
 
• Image segmentation in video sequences: A probabilistic approach with S. Russell.

In Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence (UAI). 1997. PostScript, PDF.

1996

• Context-Specific Independence in Bayesian Networks with C. Boutilier, M. Goldszmidt, and D. Koller.

In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI). 1996. PostScript, PDF.
 
• Building Classifiers using Bayesian Networks with M. Goldszmidt.

In Thirteenth National Conf. on Artificial Intelligence (AAAI). 1996.  PostScript, PDF.
 
• Discretizing Continuous Attributes While Learning Bayesian Networks with M. Goldszmidt. .

In Proc. 13'th International Conference on Machine Learning (ICML), 157-165, 1996. PostScript, PDF.
 
• Learning Bayesian Networks with Local Structure with M. Goldszmidt.

In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI). 1996. PostScript, PDF.
 
• Plausibility measures and default reasoning with J. Y. Halpern.

In Thirteenth National Conf. on Artificial Intelligence (AAAI). 1996. PostScript, PDF.
 
• A qualitative Markov assumption and its implications for belief change with J. Y. Halpern.

In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI). 1996. PostScript, PDF.
 
• Belief revision: A critique with J. Y. Halpern.

In Fifth Inter. Conf. on Knowledge Representation and Reasoning (KR). 1996. PostScript, PDF.
 
• Conditional first-order logic revisited with J. Y. Halpern, and D. Koller.

In Thirteenth National Conf. on Artificial Intelligence (AAAI). 1996. PostScript, PDF.
 
• On the Sample Complexity of Learning Bayesian Networks with Z. Yakhini.

In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI). 1996. PostScript, PDF.

1995

• On decision-theoretic foundations for defaults with R. I. Brafman.

In Proc. Fourteenth International Joint Conference on Artificial Intelligence (IJCAI '95), 1995. PostScript, PDF.
 
• Plausibility Measures: A User's Guide with J. Y. Halpern.

In Proc. Eleventh Conf. on Uncertainty in Artificial Intelligence (UAI 95), 175-184. 1995. PostScript, PDF.
 

1994

• A knowledge-based framework for belief change. Part I: Foundations with J. Y. Halpern.

In R. Fagin, ed. Theoretical Aspects of Reasoning about Knowledge: Proc. Fifth Conference (TARK'94), 44-64 1994. PostScript, PDF.
 
• A knowledge-based framework for belief change. Part II: Revision and update with J. Y. Halpern.

In J. Doyle, E. Sandwell, and P. Torasso, eds. Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR'94), 1994. 190-201. PostScript, PDF.
 
• On the complexity of conditional logics with J. Y. Halpern.

In J. Doyle, E. Sandwell, and P. Torasso, eds. Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR'94), 1994. 202-213. PostScript, PDF.
 
• Conditional logics of belief change with J. Y. Halpern.

In Proc. National Conference on Artificial Intelligence (AAAI '94),1994. 915-921. PostScript, PDF.

Others

• Modeling beliefs in dynamic systems. Phd Thesis, Stanford. 1997.

Abstract, Compressed PostScript.
 
• On the power and complexity of knowledge-charts. Master's Thesis, Weizmann Institute of Science. 1992.