363 citations · 1.1k across the 50 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
Bayesian Optimization with Expensive Integrands
Saul Toscano-Palmerin, Peter I. Frazier
We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective func…
Dueling Bandits With Weak Regret
Bangrui Chen, Peter I. Frazier
We consider online content recommendation with implicit feedback through pairwise comparisons, formalized as the so-called dueling bandit problem. We study the dueling bandit probl…
The Bayesian Linear Information Filtering Problem
Bangrui Chen, Peter I. Frazier
We present a Bayesian sequential decision-making formulation of the information filtering problem, in which an algorithm presents items (news articles, scientific papers, tweets) a…
Dueling Bandits with Dependent Arms
Bangrui Chen, Peter I. Frazier
We study dueling bandits with weak utility-based regret when preferences over arms have a total order and carry observable feature vectors. The order is assumed to be determined by…
Stratified Bayesian Optimization
Saul Toscano-Palmerin, Peter I. Frazier
We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random…
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies
Weici Hu, Peter I. Frazier
We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Baye…
A Markov Decision Process Analysis of the Cold Start Problem in Bayesian Information Filtering
Xiaoting Zhao, Peter I. Frazier
We consider the information filtering problem, in which we face a stream of items, and must decide which ones to forward to a user to maximize the number of relevant items shown, m…