Ronak Mehta

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Joshua T. VogelsteinDept of Biomedical Engineering, Johns Hopkins UniversityVerified email at jhu.edu
Hayden HelmResearcher @ Helivan ResearchVerified email at helivan.io
Cencheng ShenUniversity of DelawareVerified email at udel.edu
Carey E. PriebeProfessor of Applied Mathematics and Statistics, Johns Hopkins UniversityVerified email at jhu.edu
Jayanta DeyDoctoral candidate, Biomedical Engineering, Johns Hopkins School of MedicineVerified email at jhmi.edu
Weiwei YangMicrosoft Research, CaltechVerified email at microsoft.com
Will LeVineScale AIVerified email at scale.com
Tyler TomitaJohns Hopkins UniversityVerified email at jhmi.edu
Jesús Arroyo ReliónTexas A&M UniversityVerified email at tamu.edu
Ting Xu (徐婷)Research Scientist, Child Mind Institute, NYVerified email at childmind.org
Benjamin FalkSpace Telescope Science InstituteVerified email at stsci.edu
Satish PalaniappanMachine Learning Engineer at MicrosoftVerified email at microsoft.com
Sambit PandaPhD Candidate, Johns Hopkins UniversityVerified email at jhu.edu
Ronan PerryPhD Student at the University of WashingtonVerified email at uw.edu
Jaewon ChungJohns Hopkins UniversityVerified email at jhu.edu

Ronak Mehta

Ph.D. Student, University of Washington

Verified email at uw.edu - Homepage

statistical machine learning information theory multitask learning


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hyppo: A multivariate hypothesis testing Python package S Panda, S Palaniappan, J Xiong, EW Bridgeford, R Mehta, C Shen, ... arXiv preprint arXiv:1907.02088, 2019	16	2019
Towards a theory of out-of-distribution learning A Geisa, R Mehta, HS Helm, J Dey, E Eaton, J Dick, CE Priebe, ... arXiv preprint arXiv:2109.14501, 2021	13	2021
A general approach to progressive learning JT Vogelstein, HS Helm, RD Mehta, J Dey, W Yang, B Tower, W LeVine, ... Preprint at https://arxiv. org/abs/2004.12908, 2020	6	2020
Representation ensembling for synergistic lifelong learning with quasilinear complexity JT Vogelstein, J Dey, HS Helm, W LeVine, RD Mehta, TM Tomita, H Xu, ... arXiv preprint arXiv:2004.12908, 2020	5	2020
Estimating information-theoretic quantities with uncertainty forests R Guo, R Mehta, J Arroyo, H Helm, C Shen, JT Vogelstein arXiv, arXiv: 1907.00325, 2019	5*	2019
Estimating informationtheoretic quantities with random forests R Mehta, R Guo, C Shen, J Vogelstein arXiv preprint arXiv:1907.00325, 2019	5	2019
A partition-based similarity for classification distributions HS Helm, RD Mehta, B Duderstadt, W Yang, CM White, A Geisa, ... arXiv preprint arXiv:2011.06557, 2020	4	2020
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks A Li, R Perry, C Huynh, TM Tomita, R Mehta, J Arroyo, J Patsolic, B Falk, ... arXiv preprint arXiv:1909.11799, 2019	3*	2019
Manifold forests: closing the gap on neural networks R Perry, TM Tomita, J Patsolic, B Falk, J Vogelstein	3	2019
A consistent independence test for multivariate timeseries R Mehta, C Shen, T Xu, JT Vogelstein arXiv preprint arXiv:1908.06486, 2019	3	2019
Omnidirectional transfer for quasilinear lifelong learning JT Vogelstein, J Dey, HS Helm, W LeVine, RD Mehta, A Geisa, H Xu, ... arXiv preprint arXiv:2004.12908, 2020	2	2020
Independence testing for multivariate time series R Mehta, J Chung, C Shen, T Xu, JT Vogelstein arXiv preprint arXiv:1908.06486, 2019	2	2019
Independence testing for temporal data C Shen, J Chung, R Mehta, T Xu, JT Vogelstein arXiv e-prints, arXiv: 1908.06486, 2019	2	2019
Representation Ensembling for Synergistic Lifelong Learning with Quasilinear Complexity J Dey, J Vogelstein, H Helm, W Levine, R Mehta, A Geisa, H Xu, ...		2022
Omnidirectional Transfer for Quasilinear Lifelong Learning J Dey, J Vogelstein, H Helm, W Levine, R Mehta, A Geisa, G de Ven, ...		2021
Independence Testing for Time Series RD Mehta Johns Hopkins University, 2019		2019

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