32 citations · 82 across the 15 of their papers we have counts for
19 papers
Targeted active learning for probabilistic models
Christopher Tosh, Mauricio Tec, Wesley Tansey
A fundamental task in science is to design experiments that yield valuable insights about the system under study. Mathematically, these insights can be represented as a utility or…
DIET: Conditional independence testing with marginal dependence measures of residual information
Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey +1
Conditional randomization tests (CRTs) assess whether a variable is predictive of another variable , having observed covariates . CRTs require fitting a large number of p…
Quantile regression with deep ReLU Networks: Estimators and minimax rates
Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen
Quantile regression is the task of estimating a specified percentile response, such as the median, from a collection of known covariates. We study quantile regression with rectifie…
Deep Direct Likelihood Knockoffs
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. In scientific domains, the scientist often…
Smoothed Nested Testing on Directed Acyclic Graphs
Jackson H. Loper, Lihua Lei, William Fithian +1
We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesi…
A Bayesian Model of Dose-Response for Cancer Drug Studies
Wesley Tansey, Christopher Tosh, David M. Blei
Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-respons…
Interpreting Black Box Models via Hypothesis Testing
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. In such high-stakes tasks, false discoveries…
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Wesley Tansey, Kathy Li, Haoran Zhang +4
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becom…
The Holdout Randomization Test for Feature Selection in Black Box Models
Wesley Tansey, Victor Veitch, Haoran Zhang +2
We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models. The HRT is a specialized version of the conditional randomizati…
Black Box FDR
Wesley Tansey, Yixin Wang, David M. Blei +1
Analyzing large-scale, multi-experiment studies requires scientists to test each experimental outcome for statistical significance and then assess the results as a whole. We presen…
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
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, a…
Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning
Wesley Tansey, Karl Pichotta, James G. Scott
We present an approach to deep estimation of discrete conditional probability distributions. Such models have several applications, including generative modeling of audio, image, a…
GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification
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, a…
Diet2Vec: Multi-scale analysis of massive dietary data
Wesley Tansey, Edward W. Lowe, James G. Scott
Smart phone apps that enable users to easily track their diets have become widespread in the last decade. This has created an opportunity to discover new insights into obesity and…
Better Conditional Density Estimation for Neural Networks
Wesley Tansey, Karl Pichotta, James G. Scott
The vast majority of the neural network literature focuses on predicting point values for a given set of response variables, conditioned on a feature vector. In many cases we need…
Multiscale spatial density smoothing: an application to large-scale radiological survey and anomaly detection
Wesley Tansey, Alex Athey, Alex Reinhart +1
We consider the problem of estimating a spatially varying density function, motivated by problems that arise in large-scale radiological survey and anomaly detection. In this conte…
A Fast and Flexible Algorithm for the Graph-Fused Lasso
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 constan…
Vector-Space Markov Random Fields via Exponential Families
Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala +1
We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of undirected graphical models where each variable can belong to an arbitrary vector space. VS-MRFs generalize…
False discovery rate smoothing
Wesley Tansey, Oluwasanmi Koyejo, Russell A. Poldrack +1
We present false discovery rate smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatiall…