MATH Seminar
| Title: Scalable Bayesian optimal experimental design for efficient data acquisition |
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| Seminar: CODES@Emory |
| Speaker: Peng Chen of Georgia Tech |
| Contact: Matthias Chung, matthias.chung@emory.edu |
| Date: 2022-11-10 at 10:00AM |
| Venue: MSC W301 |
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| Abstract: Bayesian optimal experimental design (OED) is a principled framework for maximizing information gained from limited data in Bayesian inverse problems. Unfortunately, conventional methods for OED are prohibitive when applied to expensive models with high-dimensional parameters. In this talk, I will present fast and scalable computational methods for large-scale Bayesian OED with infinite-dimensional parameters, including data-informed low-rank approximation, efficient offline-online decomposition, projected neural network approximation, and a new swapping greedy algorithm for combinatorial optimization. |
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