202 citations · 424 across the 11 of their papers we have counts for
11 papers
Pitfalls and Remedies for Multi-Task Bayesian Optimization
Carl Hvarfner, Sam Daulton, Max Balandat +1
Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We r…
BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
Samuel Daulton, David Eriksson, Maximilian Balandat +1
Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters being tuned come with a carefully e…
Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
Qing Feng, Samuel Daulton, Benjamin Letham +2
Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and lea…
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…
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…
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Samuel Daulton, Maximilian Balandat, Eytan Bakshy
Optimizing multiple competing black-box objectives is a challenging problem in many fields, including science, engineering, and machine learning. Multi-objective Bayesian optimizat…