Search Results for author: David H. Brookes

Found 4 papers, 1 papers with code

Contrastive losses as generalized models of global epistasis

no code implementations4 May 2023 David H. Brookes, Jakub Otwinowski, Sam Sinai

Here we demonstrate that minimizing contrastive loss functions, such as the Bradley-Terry loss, is a simple and flexible technique for extracting the sparse latent function implied by global epistasis.

A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization

no code implementations24 May 2019 David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten

We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples.

Stochastic Optimization

Conditioning by adaptive sampling for robust design

1 code implementation29 Jan 2019 David H. Brookes, Hahnbeom Park, Jennifer Listgarten

We assume access to one or more, potentially black box, stochastic "oracle" predictive functions, each of which maps from input (e. g., protein sequences) design space to a distribution over a property of interest (e. g. protein fluorescence).

Protein Design Robust Design

Design by adaptive sampling

no code implementations8 Oct 2018 David H. Brookes, Jennifer Listgarten

We present a probabilistic modeling framework and adaptive sampling algorithm wherein unsupervised generative models are combined with black box predictive models to tackle the problem of input design.

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