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A partition-based similarity for classification distributions


Nov 12, 2020
Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt, Weiwei Yang, Christoper M. White, Ali Geisa, Joshua T. Vogelstein, Carey E. Priebe


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Robust Similarity and Distance Learning via Decision Forests


Aug 21, 2020
Tyler M. Tomita, Joshua T. Vogelstein

* Submitted to NeurIPS 2020 

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mvlearn: Multiview Machine Learning in Python


May 25, 2020
Ronan Perry, Gavin Mischler, Richard Guo, Theo Lee, Alexander Chang, Arman Koul, Cameron Franz, Joshua T. Vogelstein

* 6 pages, 2 figures, 1 table 

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Learning to rank via combining representations


May 20, 2020
Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park, Joshua T. Vogelstein, Michael Winding, Marta Zlatic, Albert Cardona, Patrick Bourke, Jonathan Larson, Chris White, Carey E. Priebe

* 10 pages, 4 figures 

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A general approach to progressive learning


Apr 28, 2020
Joshua T. Vogelstein, Hayden S. Helm, Ronak D. Mehta, Jayanta Dey, Weiwei Yang, Bryan Tower, Will LeVine, Jonathan Larson, Chris White, Carey E. Priebe


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A New Age of Computing and the Brain


Apr 27, 2020
Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, Joshua T. Vogelstein

* A Computing Community Consortium (CCC) workshop report, 24 pages 

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The Chi-Square Test of Distance Correlation


Jan 22, 2020
Cencheng Shen, Joshua T. Vogelstein

* 12 pages + 8 pages appendix, 3 figures 

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The Exact Equivalence of Independence Testing and Two-Sample Testing


Oct 20, 2019
Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein

* 27 pages main + 7 pages appendix 

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AutoGMM: Automatic Gaussian Mixture Modeling in Python


Oct 04, 2019
Thomas L. Athey, Joshua T. Vogelstein


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Manifold Forests: Closing the Gap on Neural Networks


Sep 25, 2019
Ronan Perry, Tyler M. Tomita, Jesse Patsolic, Benjamin Falk, Joshua T. Vogelstein

* 12 pages, 4 figures 

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mgcpy: A Comprehensive High Dimensional Independence Testing Python Package


Jul 18, 2019
Sambit Panda, Satish Palaniappan, Junhao Xiong, Ananya Swaminathan, Sandhya Ramachandran, Eric W. Bridgeford, Cencheng Shen, Joshua T. Vogelstein

* 15 pages, 5 figures 

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Geodesic Learning via Unsupervised Decision Forests


Jul 05, 2019
Meghana Madhyastha, Percy Li, James Browne, Veronika Strnadova-Neeley, Carey E. Priebe, Randal Burns, Joshua T. Vogelstein


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Estimating Information-Theoretic Quantities with Random Forests


Jul 03, 2019
Richard Guo, Cencheng Shen, Joshua T. Vogelstein


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GraSPy: Graph Statistics in Python


Mar 29, 2019
Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Joshua T. Vogelstein


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Decision Forests Induce Characteristic Kernels


Nov 30, 2018
Cencheng Shen, Joshua T. Vogelstein

* 9 pages, 1 figure, 1 table 

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Random Projection Forests


Oct 10, 2018
Tyler M. Tomita, James Browne, Cencheng Shen, Jesse L. Patsolic, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein

* 46 pages; submitted to Journal of Machine Learning Research for review on 09/26/2018 

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From Distance Correlation to Multiscale Graph Correlation


Sep 30, 2018
Cencheng Shen, Carey E. Priebe, Joshua T. Vogelstein

* 39 pages + Appendix 22 pages, 6 figures 

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Discovering and Deciphering Relationships Across Disparate Data Modalities


Sep 25, 2018
Cencheng Shen, Qing Wang, Eric Bridgeford, Carey E. Priebe, Mauro Maggioni, Joshua T. Vogelstein


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On a 'Two Truths' Phenomenon in Spectral Graph Clustering


Sep 07, 2018
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, Eric Bridgeford


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Kernel k-Groups via Hartigan's Method


Aug 14, 2018
Guilherme França, Maria L. Rizzo, Joshua T. Vogelstein


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The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing


Jul 09, 2018
Cencheng Shen, Joshua T. Vogelstein

* 30 pages 

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Linear Optimal Low Rank Projection for High-Dimensional Multi-Class Data


Feb 27, 2018
Joshua T. Vogelstein, Minh Tang, Eric Bridgeford, Da Zheng, Randal Burns, Mauro Maggioni

* 6 figures 

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Connectome Smoothing via Low-rank Approximations


Jan 18, 2018
Runze Tang, Michael Ketcha, Alexandra Badea, Evan D. Calabrese, Daniel S. Margulies, Joshua T. Vogelstein, Carey E. Priebe, Daniel L. Sussman

* 43 pages, 15 figures 

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Statistical inference on random dot product graphs: a survey


Sep 16, 2017
Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe

* Journal of Machine Learning Research, 2018 
* An expository survey paper on a comprehensive paradigm for inference for random dot product graphs, centered on graph adjacency and Laplacian spectral embeddings. Paper outlines requisite background; summarizes theory, methodology, and applications from previous and ongoing work; and closes with a discussion of several open problems 

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A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information


Aug 11, 2017
Kwame S. Kutten, Nicolas Charon, Michael I. Miller, J. T. Ratnanather, Jordan Matelsky, Alexander D. Baden, Kunal Lillaney, Karl Deisseroth, Li Ye, Joshua T. Vogelstein


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