Fast Interactive Image Retrieval using large-scale unlabeled data. - Akshay Mehra; Jihun Hamm; Mikhail Belkin (CoRR, 2018)

Penalty Method for Inversion-Free Deep Bilevel Optimization. - Akshay Mehra; Jihun Hamm (ACML, 2021)

How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? - Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm (CVPR, 2021)

Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. - Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm (NeurIPS, 2021)

Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks. - Byunggill Joe; Akshay Mehra; Insik Shin; Jihun Hamm (CoRR, 2021)

Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. - Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao (CoRR, 2021)

A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. - Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao (ECCV (4), 2022)

On Certifying and Improving Generalization to Unseen Domains. - Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm (CoRR, 2022)

Understanding the Robustness of Multi-Exit Models under Common Corruptions. - Akshay Mehra; Skyler Seto; Navdeep Jaitly; Barry-John Theobald (CoRR, 2022)

Analysis of Task Transferability in Large Pre-trained Classifiers. - Akshay Mehra; Yunbei Zhang; Jihun Hamm (CoRR, 2023)

Test-time Assessment of a Model's Performance on Unseen Domains via Optimal Transport. - Akshay Mehra; Yunbei Zhang; Jihun Hamm (CVPR Workshops, 2024)

Understanding the Transferability of Representations via Task-Relatedness. - Akshay Mehra; Yunbei Zhang; Jihun Hamm (NeurIPS, 2024)

On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. - Akshay Mehra; Yunbei Zhang; Bhavya Kailkhura; Jihun Hamm (WACV, 2024)

Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation. - Yunbei Zhang; Akshay Mehra; Jihun Hamm (CoRR, 2024)

A Quantitative Metric Selection Approach for Time-series Forecasting Foundation Models. - Hongjie Chen 003; Akshay Mehra; Josh Kimball; Sungchul Kim (ICASSP, 2025)

OT-VP: Optimal Transport-Guided Visual Prompting for Test-Time Adaptation. - Yunbei Zhang; Akshay Mehra; Jihun Hamm (WACV, 2025)

Model-agnostic Coreset Selection via LLM-based Concept Bottlenecks. - Akshay Mehra; Trisha Mittal; Subhadra Gopalakrishnan; Joshua Kimball (CoRR, 2025)

Measuring Time-Series Dataset Similarity using Wasserstein Distance. - Hongjie Chen 003; Akshay Mehra; Josh Kimball; Ryan A. Rossi (CoRR, 2025)