B
Ben Taskar
Researcher at University of Washington
Publications - 124
Citations - 16422
Ben Taskar is an academic researcher from University of Washington. The author has contributed to research in topics: Probabilistic logic & Structured prediction. The author has an hindex of 58, co-authored 124 publications receiving 15515 citations. Previous affiliations of Ben Taskar include Stanford University & University of California, Berkeley.
Papers
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Proceedings Article
Max-Margin Markov Networks
TL;DR: Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data, and a new theoretical bound for generalization in structured domains is provided.
Book
Introduction to statistical relational learning
Lise Getoor,Ben Taskar +1 more
TL;DR: In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data.
Journal ArticleDOI
Determinantal point processes for machine learning
Alex Kulesza,Ben Taskar +1 more
TL;DR: Determinantal Point Processes for Machine Learning provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and shows how they can be applied to real-world applications.
Proceedings Article
Discriminative probabilistic models for relational data
TL;DR: An alternative framework that builds on (conditional) Markov networks and addresses two limitations of the previous approach is presented, showing how to train these models effectively, and how to use approximate probabilistic inference over the learned model for collective classification of multiple related entities.
Book
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Lise Getoor,Ben Taskar +1 more
TL;DR: This book is intended to be a guide to the art of self-consistency and should not be relied on as a substitute for professional advice on how to deal with ambiguity.