Wesley Tansey

UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples

A. X. Xie, W. Tansey†, E. Reznik†

[paper] [code]

A Bayesian active learning platform for scalable combination drug screens

C. Tosh, M. Tec, J. White, J.F. Quinn, G. Ibanez Sanchez, P. Calder, A.L. Kung, F.S. Dela Cruz, W. Tansey

[paper] [code]

Scalable Causal Structure Learning via Amortized Conditional Independence Testing

J. Leiner, B. Manzo, A. Ramdas, W. Tansey

[paper] [code]

BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment

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

Cell Systems, 2023.

[paper] [code]

Immunometabolic coevolution defines unique microenvironmental niches in ccRCC

C. Tang, A. X. Xie, E. M. Liu, F. Kuo, M. Kim, R. G. DiNatale, M. Golkaram, Y. Chen, S. Gupta, R. J. Motzer, P. Russo, J. Coleman, M. I. Carlo, M. H. Voss, R. R. Kotecha, C. Lee, W. Tansey, N. Schultz, A. A. Hakimi, E. Reznik

Cell Metabolism, 2023.

[paper]

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

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

AI-STATS, 2023.

[paper]

Quantile Regression with ReLU Networks: Estimators and minimax rates

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

Journal of Machine Learning Research, 2022.

[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.

[paper] [code]

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.

[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]