45 citations · 106 across the 26 of their papers we have counts for
5 papers · 1 filter
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Samuel Daulton, Shaun Singh, Vashist Avadhanula +2
Recent advances in contextual bandit optimization and reinforcement learning have garnered interest in applying these methods to real-world sequential decision making problems. Rea…
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel R. Jiang +4
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental…
PlanAlyzer: Assessing Threats to the Validity of Online Experiments
Emma Tosch, Eytan Bakshy, Emery D. Berger +2
Online experiments are ubiquitous. As the scale of experiments has grown, so has the complexity of their design and implementation. In response, firms have developed software frame…
Shrinkage Estimators in Online Experiments
Drew Dimmery, Eytan Bakshy, Jasjeet Sekhon
We develop and analyze empirical Bayes Stein-type estimators for use in the estimation of causal effects in large-scale online experiments. While online experiments are generally t…
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Benjamin Letham, Eytan Bakshy
Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional p…