Pre-prints

1. K. Konishcheva, B. Leventhal, M. Koyama, S. Panda, J. T. Vogelstein, M. Milham, A. Lindner, and A. Klein. Accurate and efficient data-driven psychiatric assessment using machine learning. PsyArXiv, 2024.
2. J. Chung, E. W. Bridgeford, M. Powell, D. Pisner, T. Xu, and J. T. Vogelstein. Are human connectomes heritable? bioRxiv, 2024.
3. S. Panda, C. Shen, R. Perry, J. Zorn, A. Lutz, C. E. Priebe, and J. T. Vogelstein. High-Dimensional and Universally Consistent K-Sample Tests. arXiv, 2023.
4. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv, 2023.
5. E. W. Bridgeford, M. Powell, G. Kiar, S. Noble, J. Chung, S. Panda, R. Lawrence, T. Xu, M. Milham, B. Caffo, and J. T. Vogelstein. Batch Effects are Causal Effects: Applications in Human Connectomics. bioRxiv, 2023.
6. Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, and Joshua T. Vogelstein. Learning sources of variability from high-dimensional observational studies. arXiv, 2023.
7. J. Dey, W. LeVine, H. Xu, A. De Silva, T. M. Tomita, A. Geisa, T. Chu, J. Desman, and J. T. Vogelstein. Deep Discriminative to Kernel Generative Networks for In- and Out-of-distribution Calibrated Inference. arXiv, 2022.
8. 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.
9. Haoyin Xu, Jayanta Dey, Sambit Panda, and Joshua T. Vogelstein. Simplest Streaming Trees. arXiv, 2021.
10. Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth and 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.
11. Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell and Hayden Helm, Cencheng Shen, and Joshua T. Vogelstein. Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities. arXiv, 2021.
12. S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, . Mehta, C. Shen, and J. T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package. arXiv, 2021.
13. Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe, and Joshua T. Vogelstein. Graph Matching via Optimal Transport. arXiv, 2021.
14. Jayanta Dey, Ali Geisa, Ronak Mehta, Tyler M. Tomita, Hayden S. Helm, Eric Eaton, Jeffery Dick, Carey E. Priebe, and Joshua T. Vogelstein. Towards a theory of out-of-distribution learning. 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. Discovery of Multi-Level Network Differences Across Populations of Heterogeneous Connectomes. 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. M. Madhyastha, K. Lillaney, J. Browne, J. Vogelstein, and R. Burns. PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment. arXiv, 2020.
18. S. Shen and C. Cencheng. High-Dimensional Independence Testing via Maximum and Average Distance Correlations. arXiv, 2020.
19. J. T. Vogelstein, J. Dey, H. S. Helm, W. LeVine, Mehta, Ronak D, T. M. Tomita, H. Xu, A. Geisa, Q. Wang, . M. van de Ven, and others. A Simple Lifelong Learning Approach. arXiv, 2020.