5 papers · 1 filter
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…
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…
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
Hongseok Namkoong, Samuel Daulton, Eytan Bakshy
Thompson sampling (TS) has emerged as a robust technique for contextual bandit problems. However, TS requires posterior inference and optimization for action generation, prohibitin…