Magerman Term Assistant Professor   Computer and Information Science University of Pennsylvania 3330 Walnut Street Philadelphia, PA 19104-6389

Office:  464 GRW (N. Levine)  Phone:  215-898-7703  Fax:      215-573-8190

Teaching

Fall 2009 - CIS 520 - Machine Learning
Spring 2009 - CIS 620 - Advanced Topics in Artificial Intelligence - Probabilistic Graphical Models
Fall 2008 - CIS 520 - Machine Learning
Spring 2008 - CIS 700 - Advanced Topics in Machine Learning
Fall 2007 - CIS 521 - Fundamentals of Artificial Intelligence
Spring 2007 - CIS 620 - Advanced Topics in Artificial Intelligence

Research Group

Postdoc

Umar Syed

PhD

Timothee Cour (graduated, now a postdoc at ENS/INRIA, Paris)

Kuzman Ganchev (co-advised with Fernando Pereira)

Jennifer Gillenwater

João Graça. (visiting from INESC-ID, Lisboa)

Ben Sapp

David Weiss (co-advised with Michael Kearns)

Undergraduate

Chris Jordan

Andrew Matas

Jiawei Matteus Pan

Publications

Posterior vs. Parameter Sparsity in Latent Variable Models, K. Ganchev, J.Graça , B. Taskar and F. Pereira. Neural Information Processing Systems Conference (NIPS), Vancouver, BC, December 2009.
Supplementary Materials

Learning from Ambiguously Labeled Images, T. Cour, B. Sapp, C. Jordan and B. Taskar. Computer Vision and Pattern Recognition (CVPR), Florida, June 2009.
[Tech Report]

Learning Sparse Markov Network Structure via Ensemble-of-Trees Models, Y. Lin, S. Zhu, D. Lee, B. Taskar. Artificial Intelligence and Statistics (AISTATS), Florida, April 2009.

Joint Covariate Selection and Joint Subspace Selection for Multiple Classification Problems, G. Obozinski, B. Taskar, and M. Jordan. Journal of Statistics and Computing, to appear 2009.

Movie/Script: Alignment and Parsing of Video and Text Transcription, T. Cour, C. Jordan, E. Miltsakaki, B. Taskar. European Conference on Computer Vision (ECCV), Marseille, France, October 2008.
Video demos

Multi-View Learning over Structured and Non-Identical Outputs, K. Ganchev, J. Graça , J. Blitzer and B. Taskar. Uncertainty in Artificial Intelligence (UAI), Helsinki, Finland, July 2008.

Better Alignments = Better Translations?, K. Ganchev, J. Graça and B. Taskar. Association for Computational Linguistics (ACL), Columbus, Ohio, June 2008.
Code available: Constrained Alignment Toolkit

Online, Self-supervised Terrain Classification via Discriminatively Trained Submodular Markov Random Fields, P. Vernaza, B. Taskar and D. Lee. International Conference on Robotics and Automation (ICRA). Pasadena, California, May 2008.

Expectation Maximization and Posterior Constraints, K. Ganchev, João Graça and B. Taskar. Neural Information Processing Systems Conference (NIPS), Vancouver, BC, December 2007.

Book: Introduction to Relational Statistical Learning, Edited by L. Getoor and B. Taskar. MIT Press, November 2007.

Book: Predicting Structured Data, Edited by G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar and S. V. N. Vishwanathan. MIT Press, September 2007.

Mixture-of-Parents Maximum Entropy Markov Models, D. Rosenberg, D. Klein and B. Taskar. Uncertainty in Artificial Intelligence (UAI), Vancouver, BC, July 2007.

A Permutation-Augmented Sampler for Dirichlet Process Mixture Models, P. Liang, M. Jordan and B. Taskar. International Conference on Machine Learning (ICML), Corvalis, OR, June 2007.

An End-to-End Discriminative Approach to Machine Translation, P. Liang, Alexandre Bouchard-Cote, D. Klein and B. Taskar. Association for Computational Linguistics (ACL06), Sydney, Australia, July 2006.

Alignment by Agreement, P. Liang, B. Taskar, and D. Klein. Human Language Technology conference - North American chapter of the Association for Computational Linguistics (HLT-NAACL06), New York, June 2006.

Word Alignment via Quadratic Assignment,  S. Lacoste-Julien, B. Taskar, D. Klein, and M. Jordan. Human Language Technology conference - North American chapter of the Association for Computational Linguistics (HLT-NAACL06), New York, June 2006.

Max-Margin Markov Networks, B. Taskar, C. Guestrin, V. Chatalbashev and D. Koller. Journal of Machine Learning Research (JMLR), to appear.

Structured Prediction via the Extragradient Method, B. Taskar, S. Lacoste-Julien, and M. Jordan, Neural Information Processing Systems Conference (NIPS05), Vancouver, British Columbia, December 2005. [Longer version]

A Discriminative Matching Approach to Word Alignment, B. Taskar, S. Lacoste-Julien, and D. Klein, Empirical Methods in Natural Language Processing (EMNLP05), Vancouver, British Columbia, October 2005.

Tutorial: Max-Margin Methods for NLP: Estimation, Structure, and Applications. The Association for Computational Linguistics (ACL05), Ann Arbor, MI, June 2005.

Learning Structured Prediction Models: A Large Margin Approach.  B. Taskar, V. Chatalbashev, D. Koller and C. Guestrin. Twenty Second International Conference on Machine Learning (ICML05), Bonn, Germany, August 2005.

Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data.   D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, A. Ng. International Conference on Computer Vision and Pattern Recognition (CVPR05), San Diego, CA, June 2005.
See 3D Segmentation Project Page

Thesis: Learning Structured Prediction Models: A Large Margin Approach. Stanford University, CA, December 2004.

Exponentiated gradient algorithms for large-margin structured classification,  P. Bartlett, M. Collins, B. Taskar and D. McAllester. Neural Information Processing Systems Conference (NIPS04), Vancouver, Canada, December 2004.

Max-Margin Parsing,  B. Taskar, D. Klein, M. Collins, D. Koller and C. Manning. Empirical Methods in Natural Language Processing (EMNLP04), Barcelona, Spain, July 2004. Received best paper award.

Learning Associative Markov Networks,  B. Taskar, V. Chatalbashev and D. Koller. Twenty First International Conference on Machine Learning (ICML04), Banff, Canada, July 2004.

Max-Margin Markov Networks,  B. Taskar, C. Guestrin and D. Koller. Neural Information Processing Systems Conference (NIPS03), Vancouver, Canada, December 2003. Received best student paper award.
OCR dataset from the paper

Link Prediction in Relational Data,  B. Taskar, M. F. Wong, P. Abbeel and D. Koller. Neural Information Processing Systems Conference (NIPS03), Vancouver, Canada, December 2003.

Learning on the Test Data: Leveraging Unseen Features, B. Taskar, M. F. Wong and D. Koller. Twentieth International Conference on Machine Learning (ICML03), Washington, DC, August 2003.

Discriminative Probabilistic Models for Relational Data,  B. Taskar, P. Abbeel and D. Koller. Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI02), Edmonton, Canada, August 2002.

Learning Probabilistic Models of Link Structure, L. Getoor, N. Friedman, D. Koller and B. Taskar. Journal of Machine Learning Research (JMLR), 2002.

Probabilistic Clustering in Relational Data,   B. Taskar, E. Segal, and D. Koller. Seventeenth International Joint Conference on Artificial Intelligence (IJCAI01), Seattle, Washington, August 2001.

Probabilistic Models of Text and Link Structure for Hypertext Classification,,   L. Getoor, E. Segal, B. Taskar, D. Koller. IJCAI01 Workshop on "Text Learning: Beyond Supervision", Seattle, Washington, August 2001.

Rich Probabilistic Models for Gene Expression,  E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Ninth International Conference on Intelligent Systems For Molecular Biology (ISMB01), Copenhagen, Denmark, July 2001.

Learning Probabilistic Models of Relational Structure, L. Getoor, N. Friedman, D. Koller and B. Taskar. Eighteenth International Conference on Machine Learning (ICML01), Williamstown, Massachusetts, June 2001.

Selectivity Estimation using Probabilistic Models,  L. Getoor, B. Taskar and D. Koller, ACM SIGMOD01 International Conference on Management of Data, Santa Barbara, California, May 2001.