Luigi Nardi - Harnessing new information in Bayesian optimization
Channel: Secondmind
Date: 2023-06-16

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.

References:
- "Joint Entropy Search For Maximally-Informed Bayesian Optimization", C. Hvarfner, F. Hutter, and L. Nardi, NeurIPS (2022); https://arxiv.org/pdf/2206.04771.
- "Self-Correcting Bayesian Optimization through Bayesian Active Learning”, Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi arXiv (2023); https://arxiv.org/pdf/2304.11005. 

Speaker: 
 Luigi Nardi (Lund University, Stanford University and DBtune, www.dbtune.ai). Google scholar profile can be found at https://scholar.google.com/citations?user=Kgs3zQoAAAAJ&hl=en&oi=ao.

This talk was given at Secondmind Labs, as a part of our (virtual) research seminar. Our research seminar is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well. You can find a complete list of speakers at https://www.secondmind.ai/labs/seminars/. Learn more about Secondmind Labs at https://www.secondmind.ai/labs/​