High-Dimensional Black-Box Optimisation in Small Data Regimes | Haitham Bou Ammar

ICARL Seminar Series - 2022 Spring High-Dimensional Black-Box Optimisation in Small Data Regimes Seminar by Haitham Bou Ammar Abstract: Many problems in science and engineering can be viewed as instances of black-box optimisation over high-dimensional (structured) input spaces. Applications are ubiquitous, including arithmetic expression formation from formal grammars and property-guided molecule generation, to name a few. Machine learning (ML) has shown promising results in many such problems (sometimes) leading to state-of-the-art results. Abide those successes, modern ML techniques are data-hungry, requiring hundreds of thousands if not millions of labelled data. Unfortunately, many real-world applications do not enjoy such a luxury -- it is challenging to acquire millions of wet-lab experiments when designing new molecules. This talk will elaborate on novel techniques we developed for high-dimensional Bayesian optimisation (BO), capable of efficiently resolving such data bottlenecks. Our methods combine ideas from deep metric learning with BO to enable sample efficient low-dimensional surrogate optimisation. We provide theoretical guarantees demonstrating vanishing regrets with respect to the true high-dimensional optimisation problem. Furthermore, in a set of experiments, we confirm the effectiveness of our techniques in reducing sample sizes by acquiring state-of-the-art logP molecule values utilising only 1% labels compared to previous SOTA. —————————————————— Links Haitham Bou Ammar Site: bouammar.com/ Twitter: twitter.com/hbouammar ICARL Site: icarl.doc.ic.ac.uk Twitter: twitter.com/ic_arl YouTube: youtube.com/ICARLSeminars —————————————————— Intro and Outro music courtesy of Bensound.com - Funky Suspense by Benjamin Tissot