363 citations · 1.1k across the 50 of their papers we have counts for
54 papers
Asymptotically Optimal Regret for Black-Box Predict-then-Optimize
Samuel Tan, Peter I. Frazier
We consider the predict-then-optimize paradigm for decision-making in which a practitioner (1) trains a supervised learning model on historical data of decisions, contexts, and rew…
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Qian Xie, Raul Astudillo, Peter I. Frazier +2
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite re…
Multi-Armed Bandits with Interference
Su Jia, Peter Frazier, Nathan Kallus
Experimentation with interference poses a significant challenge in contemporary online platforms. Prior research on experimentation with interference has concentrated on the final…
Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Jiayue Wan, Raul Astudillo +3
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of functio…
Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation
Adelinde Uhrmacher, Peter Frazier, Reiner Hähnle +9
Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices a…
Modeling the Risk of In-Person Instruction during the COVID-19 Pandemic
Brian Liu, Yujia Zhang, Shane G. Henderson +2
During the COVID-19 pandemic, safely implementing in-person indoor instruction was a high priority for universities nationwide. To support this effort at the University, we develop…
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy +1
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback. This work introduces the expected utili…
Smooth Non-Stationary Bandits
Su Jia, Qian Xie, Nathan Kallus +1
In many applications of online decision making, the environment is non-stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches…
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…
Bayesian Optimization of Function Networks
Raul Astudillo, Peter I. Frazier
We consider Bayesian optimization of the output of a network of functions, where each function takes as input the output of its parent nodes, and where the network takes significan…
Two-step Lookahead Bayesian Optimization with Inequality Constraints
Yunxiang Zhang, Xiangyu Zhang, Peter I. Frazier
Recent advances in computationally efficient non-myopic Bayesian optimization (BO) improve query efficiency over traditional myopic methods like expected improvement while only mod…
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat +2
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimi…
Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors
Jiayue Wan, Yujia Zhang, Peter I. Frazier
Population-wide screening is a powerful tool for controlling infectious diseases. Group testing enables such screening despite limited resources. Viral concentration of pooled samp…
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…
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…
Matching Queues, Flexibility and Incentives
Chiwei Yan, Francisco Castro, Peter Frazier +2
Problem definition: In many matching markets, some agents are fully flexible, while others only accept a subset of jobs. For example, ridesharing drivers can specify on the platfor…
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…
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…
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…
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…