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
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 • 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.
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.
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 • 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 • 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 • 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.
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
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
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 • 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 • 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 • 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.
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 • 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.