DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation

no code implementations • 16 Feb 2024 • Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva, Alexey Kurakin, Ibrahim Hammoud, Raman Arora

To address this challenge, we first establish a generalization bound for the adversarial target loss, which consists of (i) terms related to the loss on the data, and (ii) a measure of worst-case domain divergence.

Adversarial Robustness Unsupervised Domain Adaptation

EventScore: An Automated Real-time Early Warning Score for Clinical Events

no code implementations • 11 Feb 2021 • Ibrahim Hammoud, Prateek Prasanna, IV Ramakrishnan, Adam Singer, Mark Henry, Henry Thode

We also show that discretization improves model performance by comparing our model to a baseline logistic regression model.

Mortality Prediction regression