Abstract

This seminar delves into two key advancements that contribute to the capabilities of Bayesian optimization methods. We first focus on a novel information-theoretical acquisition function called joint entropy search (JES) which introduces a new quantity to improve statistical efficiency. We then explore the integration of active learning techniques and propose self-correcting Bayesian optimization (SCoreBO) which consciously learns the model hyperparameters while optimizing the black-box function. We emphasize the benefits of adaptively selecting informative data points and address the challenges related to model misspecification.

Notes

• References:

  • "Joint Entropy Search For Maximally-Informed Bayesian Optimization", C. Hvarfner, F. Hutter, and L. Nardi, NeurIPS (2022).

  • "Self-Correcting Bayesian Optimization through Bayesian Active Learning”, Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi arXiv (2023).

• Personal website can be found here .