Title: Bayesian Optimization with Categorical and Continuous Variables
The presentation slides are available here.
Title: Bayesian Optimization with Categorical and Continuous
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  and how to scale it up to higher dimensions  and population-based AutoRL setting .
 B. Ru, A. Alvi, V. Nguyen, M. Osborne, and S. Roberts. “Bayesian optimisation over multiple continuous and categorical inputs.” ICML 2020.
 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
 J. Parker-Holder, V. Nguyen, S. Desai, and S. Roberts. “Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL.” NeurIPS 2021.
Dr. Vu Nguyen, Machine Learning Scientist, Amazon Adelaide
|Date||November 16, 2021 (Tue) 16:00 - 17:00|