Robust Design Discovery and Exploration in Bayesian Optimization

Robust Design Discovery and Exploration in Bayesian Optimization –

A Google TechTalk, presented by Ilija Bogunovic, 2022/10/04

BayesOpt Speaker Series – ABSTRACT: Whether in biological design, causal discovery, material production, or physical sciences, one often faces decisions regarding which new data to collect or which experiments to perform. There is thus a pressing need for adaptive algorithms and sampling strategies that make intelligent decisions about data collection processes and allow for data-efficient and robust learning. In this talk, I will discuss some of the core questions related to these requirements. For instance, how can we use data-driven methods to quantify uncertainty in our optimization objective and efficiently learn and discover robust designs? How can we design learning-based decision-making methods that are robust against input perturbations, data shifts, and adversarial attacks? How can we efficiently search for policies that are robust to various forms of uncertainties? In the context of previous questions, I will discuss the key statistical and robustness challenges through the lens of Bayesian optimization. I will show the limitations of existing Bayesian optimization and bandit approaches in failing to simultaneously achieve robustness and data efficiency and discuss algorithms that effectively overcome these challenges. These algorithms are robust, data-efficient, and attain rigorous theoretical guarantees. I will also demonstrate their robust performance in several applications by using real-world data sets and popular benchmarks.

Ilija Bogunovic is an Assistant Professor in the Electrical Engineering Department at the University College London. Before that, he was a postdoctoral researcher in the Machine Learning Institute and Learning and Adaptive Systems group at ETH Zurich. He received a Ph.D. in Computer and Communication Sciences from EPFL and an MSc in Computer Science from ETH Zurich.

His research interests are centered around data-efficient interactive machine learning, sequential decision making under uncertainty, reliability and robustness considerations in data-driven algorithms, experimental design and active learning methods, and are motivated by a range of emerging real-world applications. He co-founded a recurring ICML workshop on “Adaptive Experimental Design and Active Learning in the Real World”.

Seen on Youtube!