arXiv Machine Learning By Vincent D. Zaballa, Elliot E. Hui

Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design

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arXiv:2502. 08004v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems.

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arXiv Machine Learning
Jun 26

Bayesian Optimization for General Reaction Conditions

arXiv:2502. 18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation.

By Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Al\'an Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
arXiv AI
Aug 6

Out-Of-The-Loop Multi-Fidelity Bayesian Optimization

arXiv:2608. 04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available.

By Gustavo Sutter, Hao Wang, Luis Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi
arXiv Machine Learning
Jul 21

Deep Adaptive Bayesian Screening

arXiv:2607. 16927v1 Announce Type: cross Abstract: We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces.

By Jade Lejeune Herman, Arno Strouwen, Johan A. K. Suykens, Peter Goos