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Found 4 papers, 1 papers with code
Contrastive losses as generalized models of global epistasis
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
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.
Conditioning by adaptive sampling for robust design
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).
Design by adaptive sampling
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.
