363 citations · 1.1k across the 50 of their papers we have counts for
5 papers · 1 filter
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…