Search Results for author: Beau Coker

Found 7 papers, 3 papers with code

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks

no code implementations16 Nov 2022 Jiayu Yao, Yaniv Yacoby, Beau Coker, Weiwei Pan, Finale Doshi-Velez

Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in the finite-width case is intractable.

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees

no code implementations15 Apr 2022 Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Zhe Liu, Brent A. Coull

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 learning models (e. g., tree ensembles, kernel methods, neural networks, etc).

Variable Selection

Wide Mean-Field Bayesian Neural Networks Ignore the Data

1 code implementation23 Feb 2022 Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez

Finally, we show that the optimal approximate posterior need not tend to the prior if the activation function is not odd, showing that our statements cannot be generalized arbitrarily.

Variational Inference

Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data

no code implementations13 Jun 2021 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.

BIG-bench Machine Learning Variational Inference

Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks

no code implementations12 Dec 2019 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 variance.

regression

Learning a Generative Model of Cancer Metastasis

1 code implementation17 Jan 2019 Benjamin Kompa, Beau Coker

We demonstrate that our interpolations learn relevant metagenes that recapitulate known glioblastoma mechanisms and suggest possible starting points for investigations into the metastasis of SKCM into GBM.

Disentanglement General Classification

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

2 code implementations23 Apr 2018 Beau Coker, Cynthia Rudin, Gary King

We introduce hacking intervals, which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data.

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