1 code implementation • 21 Oct 2022 • Christopher Tosh, Mauricio Tec, Wesley Tansey
A fundamental task in science is to design experiments that yield valuable insights about the system under study.
no code implementations • 18 Aug 2022 • Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath
DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF).
1 code implementation • 16 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.
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.
1 code implementation • 10 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.
1 code implementation • 29 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.
1 code implementation • 13 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
3 code implementations • 1 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
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.
no code implementations • 6 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.
no code implementations • 23 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.
1 code implementation • 23 Feb 2017 • Wesley Tansey, Karl Pichotta, James G. Scott
We present an approach to deep estimation of discrete conditional probability distributions.
no code implementations • 1 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.
no code implementations • 7 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.
1 code implementation • 24 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.
1 code implementation • 19 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.
1 code implementation • 22 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