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Poster
Conditioning by adaptive sampling for robust design
David Brookes · Jennifer Listgarten
We present a method for design problems wherein the goal is to maximize or specify the value of one or more properties of interest (e.g. maximizing the fluorescence of a protein). We assume access to black box, stochastic ``oracle" predictive functions, each of which maps from design space to a distribution over properties of interest. Because many state-of-the-art predictive models are known to suffer from pathologies, especially for data far from the training distribution, the problem becomes different from directly optimizing the oracles. Herein, we propose a method to solve this problem that uses model-based adaptive sampling to estimate a distribution over the design space, conditioned on the desired properties.
Author Information
David Brookes (University of California, Berkeley)
Jennifer Listgarten (University of California, Berkeley)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Conditioning by adaptive sampling for robust design »
Wed. Jun 12th 06:40 -- 07:00 PM Room Room 201
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