Area under the ROC curve, aka AUC, is a measure of choice for assessing the performance
of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that …

Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively
studied to improve the convergence rates of stochastic non-convex optimization, which …

This paper introduces the award-winning deep learning (DL) library called LibAUC for
implementing state-of-the-art algorithms towards optimizing a family of risk functions named …

Collaborative Metric Learning (CML) has recently emerged as a popular method in
recommendation systems (RS), closing the gap between metric learning and collaborative …

The emergence of Industry 4.0 has transformed modern-day factories into high-tech
industrial sites through rapid automation and increased access to real-time data. Deep …

Model reprogramming (MR) is an emerging and powerful technique that provides cross-
domain machine learning by enabling a model that is well-trained on some source task to be …

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types
poses a challenge yet holds immense value in unraveling the molecular mechanisms …

ABSTRACT Industry 4.0 (I4. 0) has enabled dynamic modern-day industrial environments
through rapid automation and improved access to real-time data from complex industrial …

Abstract Distributionally Robust Optimization (DRO) was initially proposed as a technique to
train a model with higher weights for more difficult data samples, thereby improving model …

Abstract AUC (Area Under the Curve) is a widely used measure to evaluate the capability of
an AI model to distinguish between two classes. AUC measures the success of various …