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cs.LG2022
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
Jiaxin Zhang, Sirui Bi, Victor Fung
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a…
cs.LG2021
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizi…
cs.LG2021★ 3 cited
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intrac…