Wesley Tansey | DeepAI

DeepAI AI Chat

Featured Co-authors

David M. Blei
92 publications
Pradeep Ravikumar
79 publications
Rajesh Ranganath
56 publications
Haoran Zhang
48 publications
Oscar Hernan Madrid Padilla
34 publications
Yixin Wang
32 publications
Jesse Thomason
31 publications
Lihua Lei
24 publications
Victor Veitch
21 publications
James G. Scott
18 publications
Scott W. Linderman
18 publications

∙ 10/21/2022

Targeted active learning for probabilistic models

A fundamental task in science is to design experiments that yield valuab...

18 Christopher Tosh, et al. ∙

∙ 08/18/2022

DIET: Conditional independence testing with marginal dependence measures of residual information

Conditional randomization tests (CRTs) assess whether a variable x is pr...

0 Mukund Sudarshan, et al. ∙

∙ 10/16/2020

Quantile regression with ReLU Networks: Estimators and minimax rates

Quantile regression is the task of estimating a specified percentile res...

0 Oscar Hernan Madrid Padilla, et al. ∙

∙ 07/31/2020

Deep Direct Likelihood Knockoffs

Predictive modeling often uses black box machine learning methods, such ...

2 Mukund Sudarshan, et al. ∙

∙ 11/20/2019

Smoothed Nested Testing on Directed Acyclic Graphs

We consider the problem of multiple hypothesis testing when there is a l...

0 Wesley Tansey, et al. ∙

∙ 06/10/2019

Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices

We consider the problem of functional matrix factorization, finding low-...

8 Wesley Tansey, et al. ∙

∙ 03/29/2019

Interpreting Black Box Models with Statistical Guarantees

While many methods for interpreting machine learning models have been pr...

0 Collin Burns, et al. ∙

∙ 12/13/2018

Dose-response modeling in high-throughput cancer drug screenings: A case study with recommendations for practitioners

Personalized cancer treatments based on the molecular profile of a patie...

0 Wesley Tansey, et al. ∙

∙ 11/01/2018

The Holdout Randomization Test: Principled and Easy Black Box Feature Selection

We consider the problem of feature selection using black box predictive ...

0 Wesley Tansey, et al. ∙

∙ 06/08/2018

Black Box FDR

Analyzing large-scale, multi-experiment studies requires scientists to t...

0 Wesley Tansey, et al. ∙

∙ 08/06/2017

Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing

We consider the problem of estimating a regression function in the commo...

0 Wesley Tansey, et al. ∙

∙ 02/23/2017

GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification

We consider the problem of estimating a regression function in the commo...

0 Wesley Tansey, et al. ∙

∙ 02/23/2017

Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning

We present an approach to deep estimation of discrete conditional probab...

0 Wesley Tansey, et al. ∙

∙ 06/07/2016

Better Conditional Density Estimation for Neural Networks

The vast majority of the neural network literature focuses on predicting...

0 Wesley Tansey, et al. ∙

∙ 05/24/2015

A Fast and Flexible Algorithm for the Graph-Fused Lasso

We propose a new algorithm for solving the graph-fused lasso (GFL), a me...

0 Wesley Tansey, et al. ∙

∙ 05/19/2015

Vector-Space Markov Random Fields via Exponential Families

We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of...

0 Wesley Tansey, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world