9 citations · 13 across the 6 of their papers we have counts for
15 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…
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…