S
Sriraam Natarajan
Researcher at University of Texas at Dallas
Publications - 231
Citations - 3695
Sriraam Natarajan is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Statistical relational learning & Probabilistic logic. The author has an hindex of 28, co-authored 215 publications receiving 3145 citations. Previous affiliations of Sriraam Natarajan include Oregon State University & Wake Forest Baptist Medical Center.
Papers
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Book
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
TL;DR: This book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extensions of Bayesian networks.
Proceedings Article
Counting belief propagation
TL;DR: The counting BP algorithm as mentioned in this paper constructs a compressed factor graph of clusternodes and clusterfactors, corresponding to sets of nodes and factors that are indistinguishable given the evidence, and then runs a modified belief propagation algorithm on the compressed graph that is equivalent to running BP on the original factor graph.
Posted Content
Counting Belief Propagation
TL;DR: The experiments show that counting BP is applicable to a variety of important AI tasks such as (dynamic) relational models and boolean model counting, and that significant efficiency gains are obtainable, often by orders of magnitude.
Proceedings ArticleDOI
Dynamic preferences in multi-criteria reinforcement learning
TL;DR: This paper considers the problem of learning in the presence of time-varying preferences among multiple objectives, using numeric weights to represent their importance, and proposes a method that allows us to store a finite number of policies, choose an appropriate policy for any weight vector and improve upon it.
Journal ArticleDOI
Gradient-based boosting for statistical relational learning: The relational dependency network case
TL;DR: This work proposes to turn the problem of relational Dependency Networks into a series of relational function-approximation problems using gradient-based boosting, and shows that this boosting method results in efficient learning of RDNs when compared to state-of-the-art statistical relational learning approaches.