363 citations · 1.1k across the 50 of their papers we have counts for
5 papers · 1 filter
Regret Bounds and Experimental Design for Estimate-then-Optimize
Samuel Tan, Peter I. Frazier
In practical applications, data is used to make decisions in two steps: estimation and optimization. First, a machine learning model estimates parameters for a structural model rel…
Dynamic Pricing Provides Robust Equilibria in Stochastic Ridesharing Networks
J. Massey Cashore, Peter I. Frazier, Eva Tardos
Ridesharing markets are complex: drivers are strategic, rider demand and driver availability are stochastic, and complex city-scale phenomena like weather induce large scale correl…
Near-optimality for infinite-horizon restless bandits with many arms
Xiangyu Zhang, Peter I. Frazier
Restless bandits are an important class of problems with applications in recommender systems, active learning, revenue management and other areas. We consider infinite-horizon disc…
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
Zhiyuan Jerry Lin, Raul Astudillo, Peter I. Frazier +1
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are…
Thinking inside the box: A tutorial on grey-box Bayesian optimization
Raul Astudillo, Peter I. Frazier
Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black b…