Wesley Tansey


Targeted active learning for probabilistic models

C. Tosh, M. Tec, W. Tansey


[paper] [code]

DIET: Conditional independence testing with marginal dependence measures of residual information

M. Sudarshan, A. M. Puli, W. Tansey, and R. Ranganath


[paper] [code coming soon]

BayesTME: A unified statistical framework for spatial transcriptomics

H. Zhang, M. V. Hunter, J. Chou, J. F. Quinn, M. Zhou, R. White, and W. Tansey


[paper] [code]

Published papers (* co-first, co-senior)

Quantile Regression with ReLU Networks: Estimators and minimax rates

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

Journal of Machine Learning Research, 2022 (accepted).

[paper] [code]

MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization

B. A. Freeman*, S. Jaro*, T. Park, S. Keene, W. Tansey†, E. Reznik†

Genome Biology, 2022.

[paper] [code]

Smoothed Nested Testing on Directed Acyclic Graphs

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

Biometrika, 2021.


A Bayesian Model of Dose-Response for Cancer Drug Studies

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

Annals of Applied Statistics, 2021.

[paper] [code]

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.

[paper] [code]

RankFromSets: Scalable Set Recommendation with Optimal Recall

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

Stat, 2021.

[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, 2021.

[paper] [code]

Double Empirical Bayes Testing

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

International Statistical Review, 2020.

[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.


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.


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.


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.