no code implementations • 29 Nov 2023 • Jennifer Listgarten
Since ChatGPT works so well, are we on the cusp of solving science with AI?
no code implementations • 1 Jun 2023 • Clara Fannjiang, Jennifer Listgarten
When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure.
no code implementations • 10 Feb 2022 • Hunter Nisonoff, Yixin Wang, Jennifer Listgarten
The need for function estimation in label-limited settings is common in the natural sciences.
1 code implementation • 8 Feb 2022 • Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan
This is challenging because of a characteristic type of distribution shift between the training and test data in the design setting -- one in which the training and test data are statistically dependent, as the latter is chosen based on the former.
1 code implementation • NeurIPS 2020 • Clara Fannjiang, Jennifer Listgarten
The design goal is to construct an object with desired properties, such as a protein that binds to a therapeutic target, or a superconducting material with a higher critical temperature than previously observed.
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).
2 code implementations • NeurIPS 2018 • Francesco Paolo Casale, Adrian V. Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue.
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.
no code implementations • 3 May 2012 • Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl M. Kadie, David Heckerman
Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger data sets are used to increase power.