Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective. - Ronak Mehta; Hyunwoo J. Kim; Shulei Wang; Sterling C. Johnson; Ming Yuan 001; Vikas Singh (CoRR, 2017)

Robust Blind Deconvolution via Mirror Descent. - Sathya N. Ravi; Ronak Mehta; Vikas Singh (CoRR, 2018)

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families. - Seong Jae Hwang; Ronak Mehta; Vikas Singh (CoRR, 2018)

Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains. - Ronak Mehta; Rudrasis Chakraborty; Vikas Singh; Yunyang Xiong (ICCV, 2019)

DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer. - Haoliang Sun; Ronak Mehta; Hao Henry Zhou; Zhichun Huang; Sterling C. Johnson; Vivek Prabhakaran; Vikas Singh (ICCV, 2019)

Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? - Yunyang Xiong; Ronak Mehta; Vikas Singh (ICCV, 2019)

On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging. - Yunyang Xiong; Hyunwoo J. Kim; Bhargav Tangirala; Ronak Mehta; Sterling C. Johnson; Vikas Singh (IPMI, 2019)

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging. - Seong Jae Hwang; Ronak Mehta; Hyunwoo J. Kim; Sterling C. Johnson; Vikas Singh (UAI, 2019)

Resource Constrained Neural Network Architecture Search. - Yunyang Xiong; Ronak Mehta; Vikas Singh (CoRR, 2019)

Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks. - Jurijs Nazarovs; Ronak R. Mehta; Vishnu Suresh Lokhande; Vikas Singh (UAI, 2021)

Deep Unlearning via Randomized Conditionally Independent Hessians. - Ronak Mehta; Sourav Pal; Vikas Singh; Sathya N. Ravi (CVPR, 2022)

Investigating Functional Brain Network Abnormalities via Differential Covariance Trajectory Analysis and Scan Statistics. - Anita Sinha; Ronak Mehta; Veena A. Nair; Rasmus M. Birn; Vikas Singh; Vivek Prabhakaran (ISBI, 2022)

Stochastic Optimization for Spectral Risk Measures. - Ronak Mehta; Vincent Roulet; Krishna Pillutla; Lang Liu; Zaïd Harchaoui (AISTATS, 2023)

Robustness and Convergence of Mirror Descent for Blind Deconvolution. - Ronak Mehta; Sathya N. Ravi; Vikas Singh (ICASSP, 2023)

Efficient Discrete Multi Marginal Optimal Transport Regularization. - Ronak Mehta; Jeffery Kline; Vishnu Suresh Lokhande; Glenn Fung; Vikas Singh (ICLR, 2023)

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks. - Adam Li; Ronan Perry; Chester Huynh; Tyler M. Tomita; Ronak Mehta; Jesús Arroyo 001; Jesse Patsolic; Benjamin Falk; Sridevi V. Sarma; Joshua T. Vogelstein (SIAM J. Math. Data Sci., 2023)

Distributionally Robust Optimization with Bias and Variance Reduction. - Ronak Mehta; Vincent Roulet; Krishna Pillutla; Zaïd Harchaoui (ICLR, 2024)

The Benefits of Balance: From Information Projections to Variance Reduction. - Lang Liu; Ronak Mehta; Soumik Pal; Zaïd Harchaoui (NeurIPS, 2024)

Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization. - Ronak Mehta; Jelena Diakonikolas; Zaïd Harchaoui (NeurIPS, 2024)

A Primal-Dual Algorithm for Faster Distributionally Robust Optimization. - Ronak Mehta; Jelena Diakonikolas; Zaïd Harchaoui (CoRR, 2024)

What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text. - Arturs Kanepajs; Aditi Basu; Sankalpa Ghose; Constance Li; Akshat Mehta; Ronak Mehta; Samuel David Tucker-Davis; Bob Fischer; Jacy Reese Anthis (FAccT, 2025)

Proving the Coding Interview: A Benchmark for Formally Verified Code Generation. - Quinn Dougherty; Ronak Mehta (LLM4Code@ICSE, 2025)

What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text. - Arturs Kanepajs; Aditi Basu; Sankalpa Ghose; Constance Li; Akshat Mehta; Ronak Mehta; Samuel David Tucker-Davis; Eric Zhou; Bob Fischer (CoRR, 2025)

A Generalization Theory for Zero-Shot Prediction. - Ronak Mehta; Zaïd Harchaoui (CoRR, 2025)