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Brian Milch
Researcher at Google
Publications - 34
Citations - 3145
Brian Milch is an academic researcher from Google. The author has contributed to research in topics: Probabilistic logic & Bayesian network. The author has an hindex of 20, co-authored 34 publications receiving 3078 citations. Previous affiliations of Brian Milch include University of California, Berkeley & Stanford University.
Papers
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Multi-agent influence diagrams for representing and solving games
Daphne Koller,Brian Milch +1 more
TL;DR: This paper provides a sound and complete graphical criterion for determining strategic relevance, and shows how strategic relevance can be used to detect structure in games, allowing a large game to be broken up into a set of interacting smaller games, which can be solved in sequence.
Proceedings Article
BLOG: Probabilistic Models with Unknown Objects
TL;DR: The BLOG model as discussed by the authors is a formal language for defining probability models with unknown objects and identity uncertainty, and it can be used to describe a generative process in which some steps add objects to the world, and others determine attributes and relations on these objects.
Proceedings Article
BLOG: probabilistic models with unknown objects
TL;DR: This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty, and introduces a probabilistic form of Skolemization for handling evidence.
Proceedings Article
Identity Uncertainty and Citation Matching
TL;DR: This approach is based on the use of a relational probability model to define a generative model for the domain, including models of author and title corruption and a probabilistic citation grammar, and shows that the method outperforms current algorithms for citation matching.
Proceedings Article
Lifted probabilistic inference with counting formulas
TL;DR: This paper presents a new lifted inference algorithm, C-FOVE, that not only handles counting formulas in its input, but also creates counting formulas for use in intermediate potentials, and achieves asymptotic speed improvements compared to FOVE.