Pre-prints

1. T. Liu, J. Day, B. Xu, S. Alldritt, K. Nenning, K. Byeon, T. Xu, and J. T. Vogelstein. Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging. arXiv, 2025.
2. Q. Wang, N. Randel, Y. Yin, C. Shand, A. Strange, M. Winding, A. Cardona, M. Zlatic, J. T. Vogelstein, and C. E. Priebe. Measuring the functional complexity of nanoscale connectomes: polarity matters. bioRxiv, 2025.
3. Yuxin Bai, Aranyak Acharyya, Ashwin De Silva, Zeyu Shen, James Hassett, and Joshua T. Vogelstein. Optimal control of the future via prospective learning with control, accepted by L4DC. None, 2025.
4. 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.
5. J. Chung, E. W. Bridgeford, M. Powell, D. Pisner, T. Xu, and J. T. Vogelstein. Are human connectomes heritable? bioRxiv, 2024.
6. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv, 2023.
7. 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.
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, 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.
10. S. Panda, S. Palaniappan, J. Xiong, E. W. Bridgeford, Mehta, Ronak, C. Shen, and J. T. Vogelstein. hyppo: A Multivariate Hypothesis Testing Python Package. arXiv, 2021.