Benjamin Taskar

Publication Activity (10 Years)

Years Active: 2001-2014
Publications (10 Years): 0

Top Topics

Parameter Values

Background Knowledge

Learning Community

Point Processes

Top Venues

Publications

Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Benjamin Taskar
Expectation-Maximization for Learning Determinantal Point Processes. NIPS (2014)
Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Benjamin Taskar
Learning the Parameters of Determinantal Point Process Kernels. ICML (2014)
João Graça, Kuzman Ganchev, Luísa Coheur, Fernando Pereira, Benjamin Taskar
Controlling Complexity in Part-of-Speech Induction. J. Artif. Intell. Res. 41 (2011)
David J. Weiss, Benjamin Taskar
Structured Prediction Cascades. AISTATS (2010)
Timothée Cour, Benjamin Sapp, Chris Jordan, Benjamin Taskar
Learning from ambiguously labeled images. CVPR (2009)
Percy Liang, Michael I. Jordan, Benjamin Taskar
A permutation-augmented sampler for DP mixture models. ICML (2007)
Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan
Structured Prediction, Dual Extragradient and Bregman Projections. J. Mach. Learn. Res. 7 (2006)
Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan
Word Alignment via Quadratic Assignment. HLT-NAACL (2006)
Percy Liang, Benjamin Taskar, Dan Klein
Alignment by Agreement. HLT-NAACL (2006)
Percy Liang, Alexandre Bouchard-Côté, Dan Klein, Benjamin Taskar
An End-to-End Discriminative Approach to Machine Translation. ACL (2006)
Benjamin Taskar, Simon Lacoste-Julien, Dan Klein
A Discriminative Matching Approach to Word Alignment. HLT/EMNLP (2005)
Dragomir Anguelov, Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz, Andrew Y. Ng
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data. CVPR (2) (2005)
Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin
Learning structured prediction models: a large margin approach. ICML (2005)
Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan
Structured Prediction via the Extragradient Method. NIPS (2005)
Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester
Exponentiated Gradient Algorithms for Large-margin Structured Classification. NIPS (2004)
Benjamin Taskar, Vassil Chatalbashev, Daphne Koller
Learning associative Markov networks. ICML (2004)
Benjamin Taskar, Ming Fai Wong, Daphne Koller
Learning on the Test Data: Leveraging Unseen Features. ICML (2003)
Benjamin Taskar, Ming Fai Wong, Pieter Abbeel, Daphne Koller
Link Prediction in Relational Data. NIPS (2003)
Benjamin Taskar, Carlos Guestrin, Daphne Koller
Max-Margin Markov Networks. NIPS (2003)
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
Learning Probabilistic Models of Link Structure. J. Mach. Learn. Res. 3 (2002)
Benjamin Taskar, Pieter Abbeel, Daphne Koller
Discriminative Probabilistic Models for Relational Data. UAI (2002)
Eran Segal, Benjamin Taskar, Audrey P. Gasch, Nir Friedman, Daphne Koller
Rich probabilistic models for gene expression. ISMB (Supplement of Bioinformatics) (2001)
Lise Getoor, Benjamin Taskar, Daphne Koller
Selectivity Estimation using Probabilistic Models. SIGMOD Conference (2001)
Benjamin Taskar, Eran Segal, Daphne Koller
Probabilistic Classification and Clustering in Relational Data. IJCAI (2001)
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
Learning Probabilistic Models of Relational Structure. ICML (2001)