activity
20152021
most citedPractical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

56 citations · 99 across the 11 of their papers we have counts for

collaborators

17 papers

math.OC20219 cited

Restless Bandits with Many Arms: Beating the Central Limit Theorem

Xiangyu Zhang, Peter I. Frazier

We consider finite-horizon restless bandits with multiple pulls per period, which play an important role in recommender systems, active learning, revenue management, and many other…

stat.ML2020

Bayesian Optimization of Risk Measures

Sait Cakmak, Raul Astudillo, Peter Frazier +1

We consider Bayesian optimization of objective functions of the form , where is a black-box expensive-to-evaluate function and denotes either the VaR or CVaR…

stat.ML20197 cited

Multi-Attribute Bayesian Optimization With Interactive Preference Learning

Raul Astudillo, Peter I. Frazier

We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker (DM) whose preferences must be learned. Each feasible design is asso…

cs.GT20197 cited

Information Design in Spatial Resource Competition

Pu Yang, Krishnamurthy Iyer, Peter Frazier

We consider the information design problem in spatial resource competition settings. Agents gather at a location deciding whether to move to another location for possibly higher le…

stat.ML20196 cited

Bayesian Optimization of Composite Functions

Raul Astudillo, Peter I. Frazier

We consider optimization of composite objective functions, i.e., of the form , where is a black-box derivative-free expensive-to-evaluate function with vector-val…

cs.LG201956 cited

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier +1

Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate t…