1 citations · 1 across the 2 of their papers we have counts for
6 papers
Geometric Causal Models
Eli N. Weinstein, David M. Blei
Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. W…
Hierarchical Causal Models
Eli N. Weinstein, David M. Blei
Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in patients, or cities in states. In the…
Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
Bohan Wu, Eli N. Weinstein, Sohrab Salehi +2
Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustw…
Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
Bohan Wu, Eli N. Weinstein, David M. Blei
Empirical Bayes (EB) improves the accuracy of simultaneous inference "by learning from the experience of others" (Efron, 2012). Classical EB theory focuses on latent variables that…
Lifting Biomolecular Data Acquisition
Eli N. Weinstein, Andrei Slabodkin, Mattia G. Gollub +5
One strategy to scale up ML-driven science is to increase wet lab experiments' information density. We present a method based on a neural extension of compressed sensing to functio…
Accelerated Learning on Large Scale Screens using Generative Library Models
Eli N. Weinstein, Andrei Slabodkin, Mattia G. Gollub +1
Biological machine learning is often bottlenecked by a lack of scaled data. One promising route to relieving data bottlenecks is through high throughput screens, which can experime…