Benjamin Van Roy

About

Benjamin Van Roy is a Professor of Electrical Engineering, of Management Science and Engineering, and by courtesy, of Computer Science at Stanford University. His research focuses on areas related to electrical engineering, management science, and computer science, contributing to the academic community through his interdisciplinary expertise. As a faculty member, he is involved in advancing knowledge and education in these fields, although specific details about his research focus, background, and key contributions are not provided in the page text.

Research topics

• Artificial Intelligence
• Computer Science
• Data Mining
• Statistics
• Mathematical optimization
• Mathematics

Selected publications

• Satisficing in Time-Sensitive Bandit Learning

  Mathematics of Operations Research · 2022 · 21 citations

  Senior authorCorresponding

  Much of the recent literature on bandit learning focuses on algorithms that aim to converge on an optimal action. One shortcoming is that this orientation does not account for time sensitivity, which can play a crucial role when learning an optimal action requires much more information than near-optimal ones. Indeed, popular approaches, such as upper-confidence-bound methods and Thompson sampling, can fare poorly in such situations. We consider instead learning a satisficing action, which is nea…

  DOI

• Bayesian Reinforcement Learning With Limited Cognitive Load

  Open Mind · 2024-01-01 · 9 citations

  articleOpen accessSenior author

  Abstract All biological and artificial agents must act given limits on their ability to acquire and process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent’s learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This bod…

  PublisherOA PDFDOI

• A Definition of Continual Reinforcement Learning

  arXiv (Cornell University) · 2023-07-20 · 9 citations

  preprintOpen access

  In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on a restricted view of learning as finding a solution, rather than treating learning as endless adaptation. In contrast, continual reinforcement learning refers to the setting in which the best agents never stop learning. Despite the importance of continual reinforcement learning, the community lacks a simple definition…

  PublisherOA PDFDOI

• Deep Exploration for Recommendation Systems

  2023-09-14 · 5 citations

  articleSenior author

  Modern recommendation systems ought to benefit by probing for and learning from delayed feedback. Research has tended to focus on learning from a user’s response to a single recommendation. Such work, which leverages methods of supervised and bandit learning, forgoes learning from the user’s subsequent behavior. Where past work has aimed to learn from subsequent behavior, there has been a lack of effective methods for probing to elicit informative delayed feedback. Effective exploration through…

  PublisherDOI

• Information-Theoretic Foundations for Machine Learning

  arXiv (Cornell University) · 2024-07-17 · 2 citations

  preprintOpen accessSenior author

  The progress of machine learning over the past decade is undeniable. In retrospect, it is both remarkable and unsettling that this progress was achievable with little to no rigorous theory to guide experimentation. Despite this fact, practitioners have been able to guide their future experimentation via observations from previous large-scale empirical investigations. In this work, we propose a theoretical framework which attempts to provide rigor to existing practices in machine learning. To the…

  PublisherOA PDFDOI

Recent grants

• ITR: Axioms and Algorithms for Reputation

  NSF · $1.2M · 2004–2010

Frequent coauthors

• Ian Osband

  53 shared

• John N. Tsitsiklis

  Decision Systems (United States)

  33 shared

• Zheng Wen

  21 shared

• Gabriel Y. Weintraub

  20 shared

• C. Lanier Benkard

  18 shared

• Daniel Russo

  18 shared

• Vikranth Dwaracherla

  16 shared

• Ciamac C. Moallemi

  16 shared

Education

• Ph.D., Electrical Engineering

  Stanford University

  1990

• M.S., Electrical Engineering

  Stanford University

  1985

• B.S., Electrical Engineering

  Stanford University

  1981

Awards & honors

• MIT George C. Newton Undergraduate Laboratory Project Award
• MIT Morris J. Levin Memorial Master's Thesis Award
• MIT George M. Sprowls Doctoral Dissertation Award
• National Science Foundation CAREER Award
• Stanford Tau Beta Pi Award for Excellence in Undergraduate T…

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• Ashish Goelh-52
• Renyuan Xuh-51