15 citations · 25 across the 8 of their papers we have counts for
9 papers
Variational Deep Learning via Implicit Regularization
Jonathan Wenger, Beau Coker, Juraj Marusic +1
Modern deep learning models generalize remarkably well in-distribution, despite being overparametrized and trained with little to no explicit regularization. Instead, current theor…
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Jiayu Yao, Yaniv Yacoby, Beau Coker +2
Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in t…
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
Wenying Deng, Beau Coker, Rajarshi Mukherjee +2
We develop a simple and unified framework for nonlinear variable selection that incorporates uncertainty in the prediction function and is compatible with a wide range of machine l…
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker, Wessel P. Bruinsma, David R. Burt +2
Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provid…
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data
Beau Coker, Weiwei Pan, Finale Doshi-Velez
Variational inference enables approximate posterior inference of the highly over-parameterized neural networks that are popular in modern machine learning. Unfortunately, such post…
Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
Beau Coker, Melanie F. Pradier, Finale Doshi-Velez
While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude v…
Learning a Generative Model of Cancer Metastasis
Benjamin Kompa, Beau Coker
We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically relevant latent space of gene ex…
The age of secrecy and unfairness in recidivism prediction
Cynthia Rudin, Caroline Wang, Beau Coker
In our current society, secret algorithms make important decisions about individuals. There has been substantial discussion about whether these algorithms are unfair to groups of i…
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
Beau Coker, Cynthia Rudin, Gary King
Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along w…