47 citations · 153 across the 28 of their papers we have counts for
5 papers · 1 filter
Joint Composite Latent Space Bayesian Optimization
Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat +1
Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When d…
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Sebastian Ament, Samuel Daulton, David Eriksson +2
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often e…
Practical Policy Optimization with Personalized Experimentation
Mia Garrard, Hanson Wang, Ben Letham +9
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment. However, standard e…
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…
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
Aryan Deshwal, Sebastian Ament, Maximilian Balandat +3
We consider the problem of optimizing expensive black-box functions over high-dimensional combinatorial spaces which arises in many science, engineering, and ML applications. We us…