Search Results for author: Jennifer Listgarten

Found 10 papers, 4 papers with code

Is novelty predictable?

no code implementations1 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.

Augmenting Neural Networks with Priors on Function Values

no code implementations10 Feb 2022 Hunter Nisonoff, Yixin Wang, Jennifer Listgarten

The need for function estimation in label-limited settings is common in the natural sciences.

Conformal prediction for the design problem

1 code implementation8 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.

Conformal Prediction

Autofocused oracles for model-based design

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.


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

Gaussian Process Prior Variational Autoencoders

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.

Time Series Time Series Analysis

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.

A powerful and efficient set test for genetic markers that handles confounders

no code implementations3 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.

Two-sample testing

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