Wesley Tansey

Preprints

Quantile Regression with ReLU Networks: Estimators and minimax rates

O. H. M. Padilla, W. Tansey, and Y. Chen

Preprint.

[paper] [code]

Smoothed Nested Testing on Directed Acyclic Graphs

J. H. Loper*, L. Lei*, W. Fithian, and W. Tansey

Preprint.

[paper] [code coming soon]

A Bayesian Model of Dose-Response for Cancer Drug Studies

W. Tansey, C. Tosh, and D. M. Blei

Preprint.

[paper] [code]

Published papers

The Holdout Randomization Test for Feature Selection in Black Box Models

W. Tansey, V. Veitch, H. Zhang, R. Rabadan, and D. M. Blei

Journal of Computational and Graphical Statistics, 2021 (In press).

[paper] [code]

RankFromSets: Scalable Set Recommendation with Optimal Recall

J. Altosaar, R. Ranganath, and W. Tansey

Stat, 2021.

[paper] [code]

Double Empirical Bayes Testing

W. Tansey, Y. Wang, R. Rabadan, D. M. Blei

International Statistical Review, 2020.

[paper] [code]

Dose-Response Modeling in High-Throughput Cancer Drug Screenings: An end-to-end approach

W. Tansey, K. Li, H. Zhang, S. W. Linderman, D. M. Blei, R. Rabadan, and C. H. Wiggins

Biostatistics, 2020 (In press).

[paper] [code]

Deep Direct Likelihood Knockoffs

M. Sudarshan, W. Tansey, R. Ranganath

Neural Information Processing Systems (NeurIPS), 2020.

[paper] [code]

Interpreting Black Box Models via Hypothesis Testing

C. Burns, J. Thomason, and W. Tansey

Foundations of Data Science (FODS), 2020.

[paper] [code]

Black Box FDR

W. Tansey, Y. Wang, D. M. Blei, and R. Rabadan

International Conference on Machine Learning (ICML), 2018.

[paper] [code]

Leaf-Smoothed Hierarchical Softmax for Ordinal Prediction

W. Tansey, K. Pichotta, and J. G. Scott

AAAI Conference on Artificial Intelligence (AAAI), 2018.

[paper] [code]

Maximum-Variance Total Variation Denoising for Interpretable Spatial Smoothing

W. Tansey, J. Thomason, and J. G. Scott

AAAI Conference on Artificial Intelligence (AAAI), 2018.

[paper] [code]

False Discovery Rate Smoothing.

W. Tansey, O. Koyejo, R. A. Poldrack, and J. G. Scott.

Journal of the American Statistical Association (JASA): Theory and Methods, 2018.

[paper] [code]

Multiscale spatial density smoothing: an application to large-scale radiological survey and anomaly detection.

W. Tansey, A. Athey, A. Reinhart, and J. G. Scott

Journal of the American Statistical Association (JASA): Applications and Case Studies, 2017.

[paper] [code]

Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing

W. Tansey, J. Thomason, and J. G. Scott

ICML Workshop on Human Interpretability in Machine Learning, 2017.

Diet2Vec: Multi-scale analysis of massive dietary data

W. Tansey, E. W. Lowe, and J. G. Scott

NIPS Workshop on Machine Learning for Health, 2016.

[paper]

A Fast and Flexible Algorithm for the Graph-Fused Lasso.

W. Tansey and J. G. Scott.

Supplement to False Discovery Rate Smoothing, 2015.

[paper] [code]

Vector-Space Markov Random Fields via Exponential Families.

W. Tansey, O.-H. Madrid-Padilla, A. Suggala, and P. Ravikumar.

International Conference on Machine Learning (ICML), 2015.

[paper] [code]

Accelerating Evolution via Egalitarian Social Learning.

W. Tansey, E. Feasley, and R. Miikkulainen.

Genetic and Evolutionary Computation Conference (GECCO'12), 2012.

[paper] [code]

Multiagent learning through neuroevolution.

R. Miikkulainen, E. Feasley, L. Johnson, I. Karpov, P. Rajagopalan, A. Rawal, and W. Tansey.

Advances in Computational Intelligence, pages 24-46, 2012.

Trailblazer: A Tool for Automated Annotation Refactoring.

M. Song, E. Tilevich, and W. Tansey.

An OOPSLA 2009 Tool Demo., 2009.

DeXteR - An Extensible Framework for Declarative Parameter Passing in Distributed Object Systems.

S. Gopal, W. Tansey, G. C. Kannan, and E. Tilevich.

ACM/IFIP/USENIX International Middleware Conference (Middleware), 2008.

[paper]

Annotation Refactoring: Inferring Upgrade Transformations for Legacy Applications.

W. Tansey and E. Tilevich.

ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), 2008.

[paper]

Efficient Automated Marshaling of C++ Data Structures for MPI Applications.

W. Tansey and E. Tilevich.

IEEE International Parallel and Distributed Processing Symposium (IPDPS 2008), 2008.

[paper]