5 citations · 11 across the 4 of their papers we have counts for
8 papers
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…
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
Byunggill Joe, Jihun Hamm, Sung Ju Hwang +2
Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inpu…
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…
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Jihun Hamm, Yung-Kyun Noh
Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robus…
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…