22 citations · 34 across the 12 of their papers we have counts for
13 papers
OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation
Yunbei Zhang, Akshay Mehra, Jihun Hamm
Vision Transformers (ViTs) have demonstrated remarkable capabilities in learning representations, but their performance is compromised when applied to unseen domains. Previous meth…
DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation
Yunbei Zhang, Akshay Mehra, Shuaicheng Niu +1
Continual Test-Time Adaptation (CTTA) seeks to adapt source pre-trained models to continually changing, unseen target domains. While existing CTTA methods assume structured domain…
Test-time Assessment of a Model's Performance on Unseen Domains via Optimal Transport
Akshay Mehra, Yunbei Zhang, Jihun Hamm
Gauging the performance of ML models on data from unseen domains at test-time is essential yet a challenging problem due to the lack of labels in this setting. Moreover, the perfor…
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura +1
Achieving high accuracy on data from domains unseen during training is a fundamental challenge in domain generalization (DG). While state-of-the-art DG classifiers have demonstrate…
Understanding the Transferability of Representations via Task-Relatedness
Akshay Mehra, Yunbei Zhang, Jihun Hamm
The growing popularity of transfer learning, due to the availability of models pre-trained on vast amounts of data, makes it imperative to understand when the knowledge of these pr…
Understanding the Robustness of Multi-Exit Models under Common Corruptions
Akshay Mehra, Skyler Seto, Navdeep Jaitly +1
Multi-Exit models (MEMs) use an early-exit strategy to improve the accuracy and efficiency of deep neural networks (DNNs) by allowing samples to exit the network before the last la…
On Certifying and Improving Generalization to Unseen Domains
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen +1
Domain Generalization (DG) aims to learn models whose performance remains high on unseen domains encountered at test-time by using data from multiple related source domains. Many e…
Efficient and Local Parallel Random Walks
Michael Kapralov, Silvio Lattanzi, Navid Nouri +1
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning. Despite their relevance, the…
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen +1
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs fr…
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks
Byunggill Joe, Akshay Mehra, Insik Shin +1
Electronic Health Records (EHRs) provide a wealth of information for machine learning algorithms to predict the patient outcome from the data including diagnostic information, vita…
How Robust are Randomized Smoothing based Defenses to Data Poisoning?
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen +1
Predictions of certifiably robust classifiers remain constant in a neighborhood of a point, making them resilient to test-time attacks with a guarantee. In this work, we present a…
Penalty Method for Inversion-Free Deep Bilevel Optimization
Akshay Mehra, Jihun Hamm
Solving a bilevel optimization problem is at the core of several machine learning problems such as hyperparameter tuning, data denoising, meta- and few-shot learning, and training-…
Fast Interactive Image Retrieval using large-scale unlabeled data
Akshay Mehra, Jihun Hamm, Mikhail Belkin
An interactive image retrieval system learns which images in the database belong to a user's query concept, by analyzing the example images and feedback provided by the user. The c…