S
Stuart Russell
Researcher at University of California, Berkeley
Publications - 332
Citations - 52785
Stuart Russell is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Inference & Probabilistic logic. The author has an hindex of 72, co-authored 314 publications receiving 50018 citations. Previous affiliations of Stuart Russell include The Turing Institute & University of California.
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Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Proceedings Article
Distance Metric Learning with Application to Clustering with Side-Information
TL;DR: This paper presents an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in �”n, learns a distance metric over ℝn that respects these relationships.
Journal ArticleDOI
Algorithms for Inverse Reinforcement Learning
Andrew Y. Ng,Stuart Russell +1 more
TL;DR: Pharmacokinetics of ivermectin after IV administration were best described by a 2-compartment open model; values for main compartmental variables included volume of distribution at a steady state, area under the plasma concentration-time curve, and area underThe AUC curve.
Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.
Proceedings Article
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
TL;DR: Conditions under which modi cations to the reward function of a Markov decision process preserve the op timal policy are investigated to shed light on the practice of reward shap ing a method used in reinforcement learn ing whereby additional training rewards are used to guide the learning agent.