Optimal Gossip Algorithms for Exact and Approximate Quantile Computations. - Bernhard Haeupler; Jeet Mohapatra; Hsin-Hao Su (PODC, 2018)

Towards Verifying Robustness of Neural Networks Against Semantic Perturbations. - Jeet Mohapatra; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (CoRR, 2019)

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations. - Jeet Mohapatra; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (CVPR, 2020)

Higher-Order Certification For Randomized Smoothing. - Jeet Mohapatra; Ching-Yun Ko; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (NeurIPS, 2020)

Rethinking Randomized Smoothing for Adversarial Robustness. - Jeet Mohapatra; Ching-Yun Ko; Tsui-Wei Weng; Sijia Liu 001; Pin-Yu Chen; Luca Daniel (CoRR, 2020)

Hidden Cost of Randomized Smoothing. - Jeet Mohapatra; Ching-Yun Ko; Lily Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (AISTATS, 2021)

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. - Ching-Yun Ko; Jeet Mohapatra; Sijia Liu 001; Pin-Yu Chen; Luca Daniel; Lily Weng (ICML, 2022)

SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data. - Ching-Yun Ko; Pin-Yu Chen; Jeet Mohapatra; Payel Das; Luca Daniel (CoRR, 2022)

What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks. - Ching-Yun Ko; Pin-Yu Chen; Payel Das; Jeet Mohapatra; Luca Daniel (ICML, 2024)

Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations. - Nima Dehmamy; Csaba Both; Jeet Mohapatra; Subhro Das; Tommi Jaakkola (NeurIPS, 2024)