4 code implementations • 3 Jul 2019 • Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, Joshua T. Vogelstein
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing.
1 code implementation • CVPR 2022 • Ronak Mehta, Sourav Pal, Vikas Singh, Sathya N. Ravi
For models which require no training (k-NN), simply deleting the closest original sample can be effective.
4 code implementations • 21 Mar 2018 • Sathya N. Ravi, Ronak Mehta, Vikas Singh
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties.
1 code implementation • ICCV 2019 • Haoliang Sun, Ronak Mehta, Hao H. Zhou, Zhichun Huang, Sterling C. Johnson, Vivek Prabhakaran, Vikas Singh
Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data.
1 code implementation • 30 Jun 2019 • Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, Joshua T. Vogelstein
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.
1 code implementation • ICCV 2019 • Yunyang Xiong, Ronak Mehta, Vikas Singh
In the latter case, the optimization is often non-differentiable and also not very amenable to derivative-free optimization methods.
1 code implementation • ICCV 2019 • Ronak Mehta, Rudrasis Chakraborty, Yunyang Xiong, Vikas Singh
Using insights from differential geometry, we adapt the tensor train decomposition to construct networks with significantly fewer parameters, allowing us to train powerful recurrent networks on whole brain image volume sequences.
1 code implementation • 10 Dec 2022 • Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaid Harchaoui
Spectral risk objectives - also called $L$-risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and worst-case performance on a task.
1 code implementation • 25 Sep 2019 • Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita, Ronak Mehta, Jesus Arroyo, Jesse Patsolic, Benjamin Falk, Joshua T. Vogelstein
In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices.
no code implementations • 19 Apr 2018 • Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim, Vikas Singh
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make.
no code implementations • 20 Nov 2017 • Ronak Mehta, Hyunwoo J. Kim, Shulei Wang, Sterling C. Johnson, Ming Yuan, Vikas Singh
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources.
no code implementations • 18 Aug 2019 • Cencheng Shen, Jaewon Chung, Ronak Mehta, Ting Xu, Joshua T. Vogelstein
While many non-parametric and universally consistent dependence measures have recently been proposed, directly applying them to temporal data can inflate the p-value and result in invalid test.
no code implementations • 29 Sep 2021 • Ali Geisa, Ronak Mehta, Hayden S. Helm, Jayanta Dey, Eric Eaton, Jeffery Dick, Carey E. Priebe, Joshua T. Vogelstein
This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).
no code implementations • 21 Oct 2023 • Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaid Harchaoui
We consider the distributionally robust optimization (DRO) problem with spectral risk-based uncertainty set and $f$-divergence penalty.
no code implementations • 16 Mar 2024 • Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui
We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses the $f$-DRO, Wasserstein-DRO, and spectral/$L$-risk formulations used in practice.