no code implementations • 4 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.
no code implementations • 24 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.
1 code implementation • 29 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).
no code implementations • 8 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.