82 citations · 259 across the 11 of their papers we have counts for
11 papers
Leveraging Discrete Function Decomposability for Scientific Design
James C. Bowden, Sergey Levine, Jennifer Listgarten
In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a pro…
Is novelty predictable?
Clara Fannjiang, Jennifer Listgarten
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal implications spanning drug…
Augmenting Neural Networks with Priors on Function Values
Hunter Nisonoff, Yixin Wang, Jennifer Listgarten
The need for function estimation in label-limited settings is common in the natural sciences. At the same time, prior knowledge of function values is often available in these domai…
Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos +2
Many applications of machine learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what…
Autofocused oracles for model-based design
Clara Fannjiang, Jennifer Listgarten
Data-driven design is making headway into a number of application areas, including protein, small-molecule, and materials engineering. The design goal is to construct an object wit…
A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization
David H. Brookes, Akosua Busia, Clara Fannjiang +2
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-Maximiza…
Conditioning by adaptive sampling for robust design
David H. Brookes, Hahnbeom Park, Jennifer Listgarten
We present a new method for design problems wherein the goal is to maximize or specify the value of one or more properties of interest. For example, in protein design, one may wish…
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian V Dalca, Luca Saglietti +2
Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is th…
Design by adaptive sampling
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 pro…
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach
Jennifer Listgarden, David Heckerman
In many application domains, such as computational biology, the goal of graphical model structure learning is to uncover discrete relationships between entities. For example, in ou…
A powerful and efficient set test for genetic markers that handles confounders
Jennifer Listgarten, Christoph Lippert, Eun Yong Kang +3
Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set te…