Gal Elidan's Publications

• Learning Max-Margin Tree Predictors (pdf).
  with Ofer Meshi, Elad Eban, and Amir Globerson.
  To appear in UAI, 2013.

• Speedy Model Selection (SMS) for Copula Models (pdf).
  with Yaniv Tenzer
  To appear in UAI, 2013.

• Copulas and Machine Learning (draft).
  To appear in Lecture Notes in Statistics, 2013.

• Dynamic Copula Networks for Modeling Real-valued Time Series (pdf).
  with Elad Eban, Gideon Rothschild, Adi Mizrahi, and Israel Nelken.
  The International Conference on Artificial Intelligence and Statistics (AI-Stats), 2013.

• Nonparanormal Belief Propagation (pdf).
  with Cobi Cario.
  To appear in the Conference on Neural Information Processing Systems (NIPS), 2012.

• A Diverse Dirichlet Process Ensemble for Unsupervised Induction of Syntactic Categories (pdf).
  with Roi Reichart and Ari Rappoport.
  The International Conference on Computational Linguistics (COLING), 2012.

• Lightning-speed Structure Learning of Non-linear Continuous Networks (pdf).
  The International Conference on Artificial Intelligence and Statistics (AI-Stats), 2012.

• Copula Network Classifiers (pdf).
  The International Conference on Artificial Intelligence and Statistics (AI-Stats), 2012.

• Vocal Folds Analysis using Global Energy Tracking
  with J. Elidan.
  The Journal of Voice, 2011.

• Bagged Structure Learning of Bayesian Networks (pdf).
  The international conference on Artificial Intelligence and Statistics (AI-Stats), 2011.

• Inference-less Density Estimation using Copula Bayesian Networks (pdf).
  The Twenty Sixth Conference on Uncertainty in Artificial Intelligence (UAI), 2010.

• Copula Bayesian Networks (pdf).
  The 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010.

  Note: Theorem 3.3 has been corrected from the original version

• FastInf: An efficient Approximate Inference Library (pdf).
  with Ariel Jaimovich, Ofer Meshi and Ian McGraw.
  Journal of Machine Learning Research, 2010

• Shape-Based Object Localization for Descriptive Classification (pdf).
  with Geremy Heitz, Ben Packer, and Daphne Koller.
  International Journal of Computer Vision, 2009.

• Learning Bounded Treewidth Bayesian Networks (pdf)
  with Stephen Gould,
  Neural Information Processing Systems (NIPS) conference, 2009.
  

• Shape-Based Object Localization for Descriptive Classification (pdf)
  with Ben Packer, Geremy Heitz, and Daphne Koller
  Neural Information Processing Systems (NIPS) conference, 2009. 

• Learning Bounded Treewidth Bayesian Networks (pdf).
  with Stephen Gould,
  Journal of Machine Learning Research, 2008.

• Multi-Class Segmentation with Relative Location Prior (pdf).
  with Stephen Gould, Jim Rodgers, David Cohen and Daphne Koller.
  International Journal of Computer Vision, 2008.

• Convex Point Estimation using Undirected Bayesian Transfer Hierarchies (pdf)
  with Ben Packer, Geremy Heitz, and Daphne Koller
  The Twenty Fourth Conference on Uncertainty in Artificial Intelligence (UAI), 2008.

• Markov random field based automatic image alignment for electron tomography (pdf).
  with Fernando Amat, Farshid Moussavi, Louis Comolli, Kenneth Downing and Mark Horowits.
  Journal of Structural Biology, 2007.

• "Ideal Parent" Structure Learning for Continuous Variable Networks (pdf).
  with Iftach Nachman and Nir Friedman.
  Journal of Machine Learning Research (JMLR), Vol. 8, p. 1799-1833, 2007.

• Max-margin classification of incomplete data
  with Gal Chechik, Geremy Heitz, Pieter Abeel, and Daphne Koller
  Journal of Machine Learning Research (JMLR), In Press, 2007.

• Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach (pdf).
  with Ariel Jaimovich, Hanah Margalit and Nir Friedman
  Journal of Computational Biology (JCB), Mar 2006, Vol. 13, No. 2: 145-164.

• Using Combinatorial Optimization within Max-Product Belief Propagation (pdf)
  with John Duchi, Danny Tarlow, and Daphne Koller
  Neural Information Processing Systems (NIPS) conference, 2006.

• Max-margin classification of incomplete data (pdf)
  with Gal Chechik, Geremy Heitz, Pieter Abeel, and Daphne Koller
  Neural Information Processing Systems (NIPS) conference, 2006.

• Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing (pdf)
  with Ian McGraw and Daphne Koller
  Twenty Second Conference on Uncertainty in Artificial Intelligence (UAI), 2006.

• Learning Object Shape: From Drawings to Images (pdf)
  with Geremy Heitz and Daphne Koller
  IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2006.

• Learning Hidden Variable Networks: The Information Bottleneck Approach (pdf)
  with Nir Friedman.
  Journal of Machine Learning Research (JMLR), Vol. 6, p. 81-127, 2005.

• Toward an Integrated Protein-Protein Interaction Network (pdf)
  with Ariel Jaimovich and Nir Friedman.
  The Ninth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2005 (also presented in the KEYSTONE 2005 workshop).

• CIS: Compound Importance Sampling Method for Transcription Factor Binding Site p-value Estimation (pdf)
  with Yoseph Barash, Nir Friedman and Tommy Kaplan.
  BioInformatics, Vol. 20(6), p. 839-46, 2004.

• "Ideal Parent" Structure Learning for Continuous Variable Networks (pdf)
  with Iftach Nachman and Nir Friedman.
  Twentieth Conference on Uncertainty in Artificial Intelligence (UAI), 2004. Runner-up for Best Student paper award.

• CIS: Compound Importance Sampling Method for Transcription Factor Binding Site p-value Estimation (pdf)
  with Yoseph Barash, Nir Friedman and Tommy Kaplan.
  International Conference on Intelligent Systems for Molecular Biology (ISMB), 2004.

• The Information Bottleneck Expectation Maximization Algorithm (pdf)
  with Nir Friedman,
  Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI), 2003.

• Modeling Dependencies in Protein-DNA Binding Sites (pdf) (web supplement)
  with Yoseph Barash, Nir Friedman and Tommy Kaplan.
  The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2003.

• Data Perturbation for Escaping Local Maxima in Learning (pdf) (poster)
  with Matan Ninio, Nir Friedman and Dale Schuurmans.
  The Eighteenth National Conference on Artificial Intelligence, 2002.

• Inferring Subnetworks from Perturbed Expression Profiles (pdf)
  with Dana Pe'er, Aviv Regev and Nir Friedman.
  International Conference on Intelligent Systems for Molecular Biology (ISMB), 2001. Best paper award.

• Inferring Subnetworks from Preturbed Expression Profiles (pdf)
  with Dana Pe'er, Aviv Regev and Nir Friedman
  Bioinformatics, Vol. 17:S, p. 215-224, 2001.

• Learning the Dimensionality of Hidden Variables (pdf) (poster)
  with Nir Friedman.
  The Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 2001.

• Discovering hidden variables: A structure Based-Approach (pdf) (poster)
  with Noam Lotner, Nir Friedman and Daphne Koller.
  Neural Information Processing Systems (NIPS), 2000.

Dissertation