| Towards a theory of out-of-distribution learning J Dey, A Geisa, R Mehta, TM Tomita, HS Helm, H Xu, E Eaton, J Dick, ... arXiv preprint arXiv:2109.14501, 2021 | 18 | 2021 |
| 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 | 17 | 2019 |
| Stochastic optimization for spectral risk measures R Mehta, V Roulet, K Pillutla, L Liu, Z Harchaoui International Conference on Artificial Intelligence and Statistics, 10112-10159, 2023 | 12 | 2023 |
| 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 | 8 | 2020 |
| 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 | 7 | 2020 |
| Manifold forests: closing the gap on neural networks R Perry, TM Tomita, J Patsolic, B Falk, J Vogelstein | 7 | 2019 |
| Distributionally robust optimization with bias and variance reduction R Mehta, V Roulet, K Pillutla, Z Harchaoui arXiv preprint arXiv:2310.13863, 2023 | 6 | 2023 |
| Random forests for adaptive nearest neighbor estimation of information-theoretic quantities R Perry, R Mehta, R Guo, E Yezerets, J Arroyo, M Powell, H Helm, C Shen, ... arXiv preprint arXiv:1907.00325, 2019 | 6* | 2019 |
| 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, ... SIAM Journal on Mathematics of Data Science 5 (1), 77-96, 2023 | 5* | 2023 |
| 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 | 5 | 2020 |
| Estimating informationtheoretic quantities with random forests R Mehta, R Guo, C Shen, J Vogelstein arXiv preprint arXiv:1907.00325, 2019 | 5 | 2019 |
| A consistent independence test for multivariate timeseries R Mehta, C Shen, T Xu, JT Vogelstein arXiv preprint arXiv:1908.06486, 2019 | 3 | 2019 |
| Independence testing for temporal data C Shen, J Chung, R Mehta, T Xu, JT Vogelstein Transactions on Machine Learning Research, 1908 | 3 | 1908 |
| A Primal-Dual Algorithm for Faster Distributionally Robust Optimization R Mehta, J Diakonikolas, Z Harchaoui arXiv preprint arXiv:2403.10763, 2024 | 2 | 2024 |
| 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 |
| Leveraging machine learning and accelerometry to classify animal behaviours with uncertainty M Agarwal, K Rafiq, R Mehta, B Abrahms, Z Harchaoui bioRxiv, 2024.12. 28.630628, 2024 | | 2024 |
| The Benefits of Balance: From Information Projections to Variance Reduction L Liu, R Mehta, S Pal, Z Harchaoui arXiv preprint arXiv:2408.15065, 2024 | | 2024 |
| 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 |
Joshua T. VogelsteinDept of Biomedical Engineering, Johns Hopkins UniversityVerified email at jhu.edu
