Abstract

Whether in biological design, material production, or physical sciences, one often faces decisions regarding which new data to collect or experiments to perform. There is thus a pressing need for adaptive algorithms that make confident decisions about data collection processes and enable efficient and robust learning. In this talk, I will explore the fundamental questions related to these requirements. How can we quantify uncertainty and efficiently learn to discover robust solutions? How can we learn to actively interact with expensive simulators? How can we utilize the inherent problem structure to achieve efficient learning? In light of the previous questions, I will examine the core statistical and robustness aspects through the perspective of Bayesian optimization and reinforcement learning. I will highlight the shortcomings of standard methods and present novel algorithms that come with strong theoretical guarantees. I will also showcase their robust performance in various applications by utilizing real-world data and popular benchmarks and finally map the main avenues for future research.

Notes

• Personal website can be found here.