革新知能統合研究センター

Title: Bayesian Optimization with Categorical and Continuous Variables

The presentation slides are available here.

Abstract:
Bayesian optimization (BO) has demonstrated impressive success in optimizing black-box functions. However, there are still challenges in dealing with black-boxes that include both continuous and categorical inputs. I am presenting our recent works in optimizing the mixed space of categorical and continuous variables using Bayesian optimization [1] and how to scale it up to higher dimensions [2] and population-based AutoRL setting [3].

References:
[1] B. Ru, A. Alvi, V. Nguyen, M. Osborne, and S. Roberts. “Bayesian optimisation over multiple continuous and categorical inputs.” ICML 2020.
[2] X. Wan, V. Nguyen, H. Ha, B. Ru, C. Lu, and M. Osborne. “Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces.” ICML 2021
[3] J. Parker-Holder, V. Nguyen, S. Desai, and S. Roberts. “Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL.” NeurIPS 2021.

Bio:
Dr. Vu Nguyen, Machine Learning Scientist, Amazon Adelaide