Pre-Prints


Pre-prints
  1. Haoyin Xu, Jayanta Dey, Sambit Panda, and Joshua T. Vogelstein. Streaming Decision Trees and Forests. arXiv, 2021.
  2. 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.
  3. 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.
  4. 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:1907.02088 [cs, stat], 2021.
  5. S. Panda, C. Shen, R. Perry, J. Zorn, A. Lutz, C. E. Priebe, and J. T. Vogelstein. Nonpar MANOVA via Independence Testing. arXiv:1910.08883 [cs, stat], 2021.
  6. T. L. Athey, T. Liu, B. D. Pedigo, and J. T. Vogelstein. AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python. arxiv, 2021.
  7. 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.
  8. 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.
  9. H. S. Helm, R. D. Mehta, B. Duderstadt, W. Yang, C. M. White, A. Geisa, J. T. Vogelstein, and C. E. Priebe. A partition-based similarity for classification distributions. arXiv, 2020.
  10. J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. Geometric Dimensionality Reduction for Big Data. arXiv, 2020.
  11. 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, and J. T. Vogelstein. Eliminating accidental deviations to minimize generalization error with applications in connectomics and genomics. bioRxiv, 2020.
  12. 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.
  13. C. Shen, S. Panda, and J. T. Vogelstein. Learning Interpretable Characteristic Kernels via Decision Forests. arXiv:1812.00029 [cs, stat], 2020.
  14. 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.
  15. C. E. Priebe, J. T. Vogelstein, F. Engert, and C. M. White. Modern Machine Learning: Partition & Vote. bioRxiv, 2020.
  16. 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.
  17. Tyler M. Tomita and Joshua T. Vogelstein. Robust Similarity and Distance Learning via Decision Forests. arXiv, 2020.
  18. 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.
  19. R. Mehta, C. Shen, T. Xu, and J. T. Vogelstein. A Consistent Independence Test for Multivariate Time-Series. arxiv, 2019.
  20. R. Perry, T. M. Tomita, J. Patsolic, B. Falk, and J. T. Vogelstein. Manifold Forests: Closing the Gap on Neural Networks. arXiv, 2019.
  21. H. Helm, J. V. Vogelstein, and C. E. Priebe. Vertex Classification on Weighted Networks. arXiv, 2019.
  22. J. Xiong, C. Shen, J. Arroyo, and J. T. Vogelstein. Graph Independence Testing. arXiv, 2019.
  23. D. Mhembere, D. Zheng, C. E. Priebe, J. T. Vogelstein, and R. Burns. clusterNOR: A NUMA-Optimized Clustering Framework. arxiv, 2019.
  24. 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.
  25. 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.
  26. 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.
  27. J. T. Vogelstein, E. Bridgeford, M. Tang, D. Zheng, R. Burns, and M. Maggioni. Geometric Dimensionality Reduction for Subsequent Classification. arXiv, 2018.
  28. S. Wang, C. Shen, A. Badea, C. E. Priebe, and J. T. Vogelstein. Signal Subgraph Estimation Via Vertex Screening. arXiv, 2018.
  29. R. Tang, M. Tang, J. T. Vogelstein, and C. E. Priebe. Robust Estimation from Multiple Graphs under Gross Error Contamination. arXiv, 2017.