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On Multi-information source Constraint Active Search
Gustavo Malkomes · Bolong Cheng · Santiago Miret

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in AI for Accelerated Materials Design (AI4Mat) »
Event URL: https://openreview.net/forum?id=ZB7W9XMG4sL »

Constraint active search is a promising sample-efficient multiobjective experimental design formulation that aims to aid scientists and engineers in searching for new materials. In this proposal, we extend this formulation to situations where one can obtain observations from multiple sources each with a given cost, such as when both computer simulations and a laboratory experiments can be used to calculate (or estimate) properties of a material of interest. We present a novel cost-efficient policy that balances the cost of obtaining observations with the benefit of evaluating a more expensive-to-compute source. Initial results on a synthetic problem show that our proposed methodology is more selective when searching for the most expensive source.

Keywords: Active Learning · Gaussian process · Active Search · constraint active search

Author Information

Gustavo Malkomes (Intel)
Bolong Cheng (Intel)

Hello. I am a research engineer at SigOpt (acquired by Intel in 2020). Currently, I work on productionizing Bayesian optimization, and more broadly, sequential decision making problems. I am especially interested in applying sequential optimization techniques in scientific and engineering domains such as materials simulation and design. Prior to SigOpt, I obtained my Ph.D. in electrical engineering from Princeton University, where I was advised by Prof. Warren B. Powell. My doctoral studies focused on approximate dynamic programming, stochastic optimization, and optimal learning, with an application in managing grid-level battery storage.

Santiago Miret (Intel AI Lab)

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