Timezone: »
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)
-
2019 Oral: Conditioning by adaptive sampling for robust design »
Wed. Jun 12th 06:40 -- 07:00 PM Room Room 201
More from the Same Authors
-
2020 : "Machine learning-based design (of proteins, small molecules and beyond)" »
Jennifer Listgarten -
2019 Workshop: ICML 2019 Workshop on Computational Biology »
Donna Pe'er · Sandhya Prabhakaran · Elham Azizi · Abdoulaye BanirĂ© Diallo · Anshul Kundaje · Barbara Engelhardt · Wajdi Dhifli · Engelbert MEPHU NGUIFO · Wesley Tansey · Julia Vogt · Jennifer Listgarten · Cassandra Burdziak · Workshop CompBio