Speaker |
Ilija Bogunovic |
|---|---|
Affiliation |
UCL |
Date |
Friday, 11 November 2022 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
Jump Trading/ELLIS CSML Seminar 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 exploit the problem structure for efficient learning (e.g., how to deal with graph data and permutation-invariant reward functions and provably scale to large domains and graphs)? In the context of the previous questions, I will discuss the key statistical and robustness challenges through the lens of Bayesian optimization and neural bandits. 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. |
Biography |
Ilija Bogunovic is a Lecturer 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, 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'. |