Generalization to out-of-distribution (OOD) data is one of the central problems in modern
machine learning. Recently, there is a surge of attempts to propose algorithms that mainly …

Medical image analysis is a vibrant research area that offers doctors and medical
practitioners invaluable insight and the ability to accurately diagnose and monitor disease …

This work considers the out-of-distribution (OOD) prediction problem where (1)~ the training
data are from multiple domains and (2)~ the test domain is unseen in the training. DNNs fail …

Instance-based interpretation methods have been widely studied for supervised learning
methods as they help explain how black box neural networks predict. However, instance …

Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for
capturing complex dependencies within diverse graph-structured data. Despite their …

The objective of machine unlearning (MU) is to eliminate previously learned data from a
model. However, it is challenging to strike a balance between computation cost and …

How to properly set the privacy parameter in differential privacy has been an open question
in DP research since it was first proposed in 2006. In this work, we demonstrate the ability of …

Influence functions serve as crucial tools for assessing sample influence in model
interpretation, subset training set selection, noisy label detection, and more. By employing …

Medical image analysis is a vibrant research area that offers doctors and medical
practitioners valuable insight and the ability to accurately diagnose and monitor disease …

Deep learning models used in Computer-Aided Diagnosis (CAD) systems are often trained
with Empirical Risk Minimization (ERM) loss. These models often achieve high overall …