1. J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. Geometric Dimensionality Reduction for Big Data. arXiv, 2020.
  2. E. W. Bridgeford, S. Wang, Z. Yang, Z. Wang, T. Xu, C. Craddock, J. Dey, G. Kiar, W. Gray-Roncal, C. Coulantoni, C. Douville, C. E. Priebe, B. Caffo, M. Milham, X. Zuo, (CoRR), and J. T. Vogelstein. Big Data Reproducibility: Applications in Brain Imaging and Genomics. bioRxiv, 2020.
  3. R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. A Consistent Independence Test for Multivariate Time-Series. arxiv, 2019.
  4. R. Perry, T. M. Tomita, J. Patsolic, B. Falk, and J. T. Vogelstein. Manifold Forests: Closing the Gap on Neural Networks. arXiv, 2019.
  5. M. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranada, J. N. Kather, K. Kording, B. Richards, and D. Bzdok. Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets. bioRxiv, 2019.
  6. T. M. Tomita, J. Browne, C. Shen, J. Chung, J. L. Patsolic, B. Falk, J. Yim, C. E. Priebe, R. Burns, M. Maggioni, and J. T. Vogelstein. Sparse Projection Oblique Randomer Forests. arXiv, 2019.
  7. R. Guo, C. Shen, and J. T. Vogelstein. Estimating Information-Theoretic Quantities with Random Forests. arXiv, 2019.
  8. S. Hong, J. T. Vogelstein, G. Gozzi, B. C. Bernhardt, T. B. Yeo, M. P. Milham, and A. Di Martino. Towards Neurosubtypes in Autism. bioRxiv, 2019.
  9. M. Madhyastha, P. Li, J. Browne, V. Strnadova-Neely, C. E. Priebe, R. Burns, and J. T. Vogelstein. Geodesic Learning via Unsupervised Decision Forests. arXiv, 2019.
  10. D. Mhembere, D. Zheng, J. T. Vogelstein, C. E. Priebe, and R. Burns. Graphyti: A Semi-External Memory Graph Library for FlashGraph. arXiv, 2019.
  11. A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. T. Vogelstein, and M. Milham. Bagging Improves Reproducibility of Functional Parcellation of the Human Brain. bioRxiv, 2019.
  12. S. Panda, S. Palaniappan, J. Xiong, A. Swaminathan, S. Ramachandran, E. W. Bridgeford, C. Shen, and J. T. Vogelstein. mgcpy: A Comprehensive High Dimensional Independence Testing Python Package. arXiv, 2019.
  13. N. Wang, R. J. Anderson, D. G. Ashbrook, V. Gopalakrishnan, Y. Park, C. E. Priebe, Y. Qi, J. T. Vogelstein, R. W. Williams, and A. G. Johnson. Node-Specific Heritability in the Mouse Connectome. bioRxiv, 2019.
  14. T. Xu, K. Nenning, E. Schwartz, S. Hong, J. T. Vogelstein, D. A. Fair, C. E. Schroeder, D. S. Margulies, J. Smallwood, M. P. Milham, and G. Langs. Cross-species Functional Alignment Reveals Evolutionary Hierarchy Within the Connectome. bioRxiv, 2019.
  15. J. Arroyo, A. Athreya, J. Cape, G. Chen, C. E. Priebe, and J. T. Vogelstein. Inference for multiple heterogenous networks with a common invariant subspace. arXiv, 2019.
  16. H. Helm, J. V. Vogelstein, and C. E. Priebe. Vertex Classification on Weighted Networks. arXiv, 2019.
  17. J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. Graph Independence Testing. arXiv, 2019.
  18. D. Mhembere, D. Zheng, C. E. Priebe, J. T. Vogelstein, and R. Burns. clusterNOR: A NUMA-Optimized Clustering Framework. arxiv, 2019.
  19. A. Branch, D. Tward, J. T. Vogelstein, Z. Wu, and M. Gallagher. An optimized protocol for iDISCO+ rat brain clearing, imaging, and analysis. bioRxiv, 2019.
  20. C. Shen and J. T. Vogelstein. Decision Forests Induce Characteristic Kernels. arXiv, 2018.
  21. D. S. Greenberg, D. J. Wallace, K. Voit, S. Wuertenberger, U. Czubayko, A. Monsees, T. Handa, J. T. Vogelstein, R. Seifert, Y. Groemping, and J. N. Kerr. Accurate action potential inference from a calcium sensor protein through biophysical modeling. bioRxiv, 2018.
  22. Z. Wang, H. Sair, C. Crainiceanu, M. Lindquist, B. A. Landman, S. Resnick, J. T. Vogelstein, and B. S. Caffo. On statistical tests of functional connectome fingerprinting. bioRxiv, 2018.
  23. C. Shen and J. T. Vogelstein. The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing. arXiv, 2018.
  24. G. Kiar, E. Bridgeford, W. G. Roncal, (CoRR), V. Chandrashekhar, D. Mhembere, S. Ryman, X. Zuo, D. S. Marguiles, R. C. Craddock, C. E. Priebe, R. Jung, V. Calhoun, B. Caffo, R. Burns, M. P. Milham, and J. Vogelstein. A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. bioRxiv, 2018.
  25. S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.
  26. G. Kiar, E. Bridgeford, V. Chandrashekhar, D. Mhembere, R. Burns, W. R. G. Roncal, and J. T. Vogelstein. A comprehensive cloud framework for accurate and reliable human connectome estimation and meganalysis. bioRxiv, 2017.
  27. G. Franca, M. L. Rizzo, and J. T. Vogelstein. Kernel k-Groups via Hartigan's Method. arXiv, 2017.
  28. R. Tang, M. Tang, J. T. Vogelstein, and C. E. Priebe. Robust Estimation from Multiple Graphs under Gross Error Contamination. arXiv, 2017.
  29. 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, 2014.