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Invited talk: Knowledge Gradient Methods for Bayesian Optimization
Peter Frazier
Author Information
Peter Frazier (Cornell / Uber)
Peter Frazier is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University, and a Staff Data Scientist at Uber. He received a Ph.D. in Operations Research and Financial Engineering from Princeton University in 2009. His research is at the intersection of machine learning and operations research, focusing on Bayesian optimization, multi-armed bandits, active learning, and Bayesian nonparametric statistics. He is an associate editor for Operations Research, ACM TOMACS, and IISE Transactions, and is the recipient of an AFOSR Young Investigator Award and an NSF CAREER Award.
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2021 Poster: Constrained Two-step Look-Ahead Bayesian Optimization »
Yunxiang Zhang · Xiangyu Zhang · Peter Frazier -
2021 Poster: Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs »
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2021 Poster: Bayesian Optimization of Function Networks »
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2020 Poster: Bayesian Optimization of Risk Measures »
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2019 Poster: Practical Two-Step Lookahead Bayesian Optimization »
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2017 Poster: Multi-Information Source Optimization »
Matthias Poloczek · Jialei Wang · Peter Frazier -
2017 Spotlight: Multi-Information Source Optimization »
Matthias Poloczek · Jialei Wang · Peter Frazier -
2017 Poster: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier -
2017 Oral: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier