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Bayesian Optimization over Combinatorial Structures - Aryan Deshwal

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live: https://valence-discovery.github.io/M... Title: Bayesian Optimization over Combinatorial Structures Abstract: Scientists and engineers in diverse domains need to perform expensive experiments to optimize combinatorial spaces, where each candidate input is a discrete structure (e.g., sequence, tree, graph) or a hybrid structure (mixture of discrete and continuous design variables). For example, in drug and vaccine design, we need to search a large space of molecules guided by physical lab experiments. These experiments are often performed in a heuristic manner by humans and without any formal reasoning. Bayesian optimization (BO) is an efficient framework for optimizing expensive black-box functions. However, most of the BO literature is largely focused on optimizing continuous spaces. In this talk, I will discuss the main challenges in extending BO framework to combinatorial structures and some algorithms that I have developed in addressing them. Speaker: Aryan Deshwal - https://aryandeshwal.github.io/ Twitter Prudencio:   / tossouprudencio   Twitter Therence:   / therence_mtl   Twitter Cas:   / cas_wognum   Twitter Valence Discovery:   / valence_ai   ~ Chapters: 00:00 Introduction and motivation 05:58 Background - Bayesian Optimization 12:41 LADDER 23:48 LADDER results 31:47 MerCBO 41:45 MerCBO results 44:31 Conclusion 45:52 Q&A