Search Results for author: Ronak Mehta

Found 12 papers, 8 papers with code

Deep Unlearning via Randomized Conditionally Independent Hessians

1 code implementation15 Apr 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.

Face Recognition Person Re-Identification

Towards a theory of out-of-distribution learning

no code implementations29 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).

Learning Theory

Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains

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.

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

1 code implementation25 Sep 2019 Ronan Perry, Adam Li, 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.

EEG Image Classification +1

Independence Testing for Multivariate Time Series

no code implementations18 Aug 2019 Ronak Mehta, Jaewon Chung, Cencheng Shen, Ting Xu, Joshua T. Vogelstein

The proposed nonparametric procedure is valid and consistent, building upon prior work by characterizing the geometry of the relationship, estimating the time lag at which dependence is maximized, avoiding the need for multiple testing, and exhibiting superior power in high-dimensional, low sample size, nonlinear settings.

Time Series Time Series Analysis

hyppo: A Multivariate Hypothesis Testing Python Package

4 code implementations3 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.

Two-sample testing

Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities

1 code implementation30 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.

Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?

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.

Neural Architecture Search

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families

no code implementations19 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.

Robust Blind Deconvolution via Mirror Descent

4 code implementations21 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.

Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective

no code implementations20 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.

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