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All publications by Ben Taskar
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Learning the Parameters of Determinantal Point Process Kernels
Raja H. Affandi, Emily Fox, Ryan Adams and Ben Taskar
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Expectation-Maximization for Learning Determinantal Point Processes
Jennifer A. Gillenwater, Alex Kulesza, Emily Fox and Ben Taskar
Advances in Neural Information Processing Systems 27, 2014


Collective Stability in Structured Prediction: Generalization from One Example
Ben London, Bert Huang, Ben Taskar and Lise Getoor
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Learning Adaptive Value of Information for Structured Prediction
David J. Weiss and Ben Taskar
Advances in Neural Information Processing Systems 26, 2013


Approximate Inference in Continuous Determinantal Processes
Raja H. Affandi, Emily Fox and Ben Taskar
Advances in Neural Information Processing Systems 26, 2013


The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification
Ofir Pele, Ben Taskar, Amir Globerson and Michael Werman
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Near-Optimal MAP Inference for Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza and Ben Taskar
Advances in Neural Information Processing Systems 25, 2012


k-DPPs: Fixed-Size Determinantal Point Processes
Alex Kulesza and Ben Taskar
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Sidestepping Intractable Inference with Structured Ensemble Cascades
David Weiss, Benjamin Sapp and Ben Taskar
Advances in Neural Information Processing Systems 23, 2010


Structured Determinantal Point Processes
Alex Kulesza and Ben Taskar
Advances in Neural Information Processing Systems 23, 2010


Semi-Supervised Learning with Adversarially Missing Label Information
Umar Syed and Ben Taskar
Advances in Neural Information Processing Systems 23, 2010


Structured Prediction Cascades
David Weiss and Ben Taskar
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Posterior vs Parameter Sparsity in Latent Variable Models
Kuzman Ganchev, Ben Taskar, Fernando Pereira and João Gama
Advances in Neural Information Processing Systems 22, 2009


Learning Sparse Markov Network Structure via Ensemble-of-Trees Models
Yuanqing Lin, Shenghuo Zhu, Daniel D. Lee and Ben Taskar
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


A permutation-augmented sampler for DP mixture models
Percy Liang, Michael I. Jordan and Ben Taskar
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Expectation Maximization and Posterior Constraints
Kuzman Ganchev, Ben Taskar and João Gama
Advances in Neural Information Processing Systems 20, 2007


Learning structured prediction models: a large margin approach
Vassil Chatalbashev, Daphne Koller, Carlos Guestrin and Ben Taskar
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Learning associative Markov networks
Vassil Chatalbashev, Daphne Koller and Ben Taskar
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Exponentiated Gradient Algorithms for Large-margin Structured Classification
Peter L. Bartlett, Michael Collins, Ben Taskar and David A. Mcallester
Advances in Neural Information Processing Systems 17, 2004


Learning on the Test Data: Leveraging Unseen Features
Ming F. Wong, Daphne Koller and Ben Taskar
Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003


Link Prediction in Relational Data
Ben Taskar, Ming-fai Wong, Pieter Abbeel and Daphne Koller
Advances in Neural Information Processing Systems 16, 2003


Max-Margin Markov Networks
Ben Taskar, Carlos Guestrin and Daphne Koller
Advances in Neural Information Processing Systems 16, 2003