Search Results for author: Wesley Tansey

Found 15 papers, 10 papers with code

Quantile regression with deep ReLU Networks: Estimators and minimax rates

1 code implementation16 Oct 2020 Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen

Overall, the theoretical and empirical results provide insight into the strong performance of ReLU neural networks for quantile regression across a broad range of function classes and error distributions.

Deep Direct Likelihood Knockoffs

1 code implementation NeurIPS 2020 Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath

Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance.

A Bayesian Model of Dose-Response for Cancer Drug Studies

1 code implementation10 Jun 2019 Wesley Tansey, Christopher Tosh, David M. Blei

The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases.

Denoising Drug Discovery +2

Interpreting Black Box Models via Hypothesis Testing

1 code implementation29 Mar 2019 Collin Burns, Jesse Thomason, Wesley Tansey

In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments.

Two-sample testing

Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach

1 code implementation13 Dec 2018 Wesley Tansey, Kathy Li, Haoran Zhang, Scott W. Linderman, Raul Rabadan, David M. Blei, Chris H. Wiggins

Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.

Applications

The Holdout Randomization Test for Feature Selection in Black Box Models

3 code implementations1 Nov 2018 Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei

We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.

Methodology

Black Box FDR

no code implementations ICML 2018 Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan

BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis.

Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing

no code implementations6 Aug 2017 Wesley Tansey, Jesse Thomason, James G. Scott

We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance.

Additive models Denoising

Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning

1 code implementation23 Feb 2017 Wesley Tansey, Karl Pichotta, James G. Scott

We present an approach to deep estimation of discrete conditional probability distributions.

GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification

no code implementations23 Feb 2017 Wesley Tansey, James G. Scott

We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance.

Additive models Classification +1

Diet2Vec: Multi-scale analysis of massive dietary data

no code implementations1 Dec 2016 Wesley Tansey, Edward W. Lowe Jr., James G. Scott

Smart phone apps that enable users to easily track their diets have become widespread in the last decade.

Better Conditional Density Estimation for Neural Networks

no code implementations7 Jun 2016 Wesley Tansey, Karl Pichotta, James G. Scott

CDE Trend Filtering applies a k-th order graph trend filtering penalty to the unnormalized logits of a multinomial classifier network, with each edge in the graph corresponding to a neighboring point on a discretized version of the density.

Density Estimation

A Fast and Flexible Algorithm for the Graph-Fused Lasso

1 code implementation24 May 2015 Wesley Tansey, James G. Scott

We propose a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally constant over a predefined graph structure.

Vector-Space Markov Random Fields via Exponential Families

1 code implementation19 May 2015 Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar

Specifically, VS-MRFs are the joint graphical model distributions where the node-conditional distributions belong to generic exponential families with general vector space domains.

False discovery rate smoothing

1 code implementation22 Nov 2014 Wesley Tansey, Oluwasanmi Koyejo, Russell A. Poldrack, James G. Scott

We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods.

Methodology Applications Computation

Cannot find the paper you are looking for? You can Submit a new open access paper.