1. B. D. Pedigo, M. Winding, C. E. Priebe, and J. T. Vogelstein. Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes. bioRxiv, 2022.
  2. Jayanta Dey, Ashwin De Silva, Will LeVine, Jong Shin, Haoyin Xu, Ali Geisa, Tiffany Chu, Leyla Isik, and Joshua T. Vogelstein. Out-of-distribution Detection Using Kernel Density Polytopes. arXiv, 2022.
  3. J. T. Vogelstein, T. Verstynen, K. P. Kording, L. Isik, J. W. Krakauer, R. Etienne-Cummings, E. L. Ogburn, C. E. Priebe, R. Burns, K. Kutten, J. J. Knierim, J. B. Potash, T. Hartung, L. Smirnova, P. Worley, A. Savonenko, I. Phillips, M. I. Miller, R. Vidal, J. Sulam, A. Charles, N. J. Cowan, M. Bichuch, A. Venkataraman, C. Li, N. Thakor, J. M. Kebschull, M. Albert, J. Xu, M. H. Shuler, B. Caffo, T. Ratnanather, A. Geisa, S. Roh, E. Yezerets, M. Madhyastha, J. J. How, T. M. Tomita, J. Dey, N. Huang, J. M. Shin, K. A. Kinfu, P. Chaudhari, B. Baker, A. Schapiro, D. Jayaraman, E. Eaton, M. Platt, L. Ungar, L. Wehbe, A. Kepecs, A. Christensen, O. Osuagwu, B. Brunton, B. Mensh, A. R. Muotri, G. Silva, F. Puppo, F. Engert, E. Hillman, J. Brown, C. White, and W. Yang. Prospective Learning: Back to the Future. arXiv [cs.LG], 2022.
  4. T. Xu, J. Cho, G. Kiar, E. W. Bridgeford, J. T. Vogelstein, and M. P. Milham. A Guide for Quantifying and Optimizing Measurement Reliability for the Study of Individual Differences. bioRxiv, 2022.
  5. Haoyin Xu, Jayanta Dey, Sambit Panda, and Joshua T. Vogelstein. Simplest Streaming Trees. arXiv, 2021.
  6. Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, and Carey E. Priebe. When are Deep Networks really better than Decision Forests at small sample sizes, and how? arXiv, 2021.
  7. R. Xiong, A. Koenecke, M. Powell, Z. Shen, J. T. Vogelstein, and S. Athey. Federated Causal Inference in Heterogeneous Observational Data. arXiv, 2021.
  8. Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, and Joshua T. Vogelstein. Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities. arXiv, 2021.
  9. S. Li, T. Jun, Z. Wang, Y. Kao, E. Schadt, M. F. Konig, C. Bettegowda, J. T. Vogelstein, N. Papadopoulos, R. E. Parsons, and others. COVID-19 outcomes among hospitalized men with or without exposure to alpha-1-adrenergic receptor blocking agents. medRxiv, 2021.
  10. S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, R. Mehta, C. Shen, and J. T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package. arXiv, 2021.
  11. S. Panda, C. Shen, R. Perry, J. Zorn, A. Lutz, C. E. Priebe, and J. T. Vogelstein. Nonpar MANOVA via Independence Testing. arXiv, 2021.
  12. Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, and Joshua T. Vogelstein. Towards a theory of out-of-distribution learning. arXiv, 2021.
  13. T. L. Athey, T. Liu, B. D. Pedigo, and J. T. Vogelstein. AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python. arxiv, 2021.
  14. Jaewon Chung, Bijan Varjavand, Jesus Arroyo, Anton Alyakin, Joshua Agterberg, Minh Tang, Joshua T. Vogelstein, and Carey E. Priebe. Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics. arXiv, 2021.
  15. V. Gopalakrishnan, J. Chung, E. Bridgeford, B. D. Pedigo, J. Arroyo, L. Upchurch, G. A. Johnsom, N. Wang, Y. Park, C. E. Priebe, and J. T. Vogelstein. Multiscale Comparative Connectomics. arXiv, 2020.
  16. Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, and Carey E. Priebe. Multiple Network Embedding for Anomaly Detection in Time Series of Graphs. arXiv, 2020.
  17. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv, 2020.
  18. K. Mehta, R. F. Goldin, D. Marchette, J. T. Vogelstein, C. E. Priebe, and G. A. Ascoli. Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding. bioRxiv, 2020.
  19. Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Emily Chang, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, and Carey E. Priebe. Omnidirectional Transfer for Quasilinear Lifelong Learning. arXiv, 2020.
  20. M. Madhyastha, K. Lillaney, J. Browne, J. Vogelstein, and R. Burns. PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment. arXiv, 2020.
  21. S. Shen and C. Cencheng. High-dimensional independence testing and maximum marginal correlation. arXiv, 2020.
  22. Tyler M. Tomita and Joshua T. Vogelstein. Robust Similarity and Distance Learning via Decision Forests. arXiv, 2020.
  23. E. W. Bridgeford, S. Wang, Z. Yang, Z. Wang, T. Xu, C. Craddock, G. Kiar, W. Gray-Roncal, C. E. Priebe, B. Caffo, M. Milham, X. Zuo, (CoRR), and J. T. Vogelstein. Optimal Experimental Design for Big Data: Applications in Brain Imaging. bioRxiv, 2019.
  24. R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. A Consistent Independence Test for Multivariate Time-Series. arxiv, 2019.
  25. R. Perry, T. M. Tomita, J. Patsolic, B. Falk, and J. T. Vogelstein. Manifold Forests: Closing the Gap on Neural Networks. arXiv, 2019.
  26. J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. Graph Independence Testing. arXiv, 2019.
  27. H. Patsolic, S. Adali, J. T. Vogelstein, Y. Park, C. E. Priebe, G. Li, and V. Lyzinski. Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability. arXiv, 2019.
  28. J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. Geometric Dimensionality Reduction for Subsequent Classification. arXiv, 2018.
  29. S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.