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cs.LG2026
Online Variance Reduction for Domain Adaptation on Streaming Data
Andrea Napoli
This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions. Although various offl…
cs.LG2026
Variance-reduced Domain Adaptation using Paired Sampling
Andrea Napoli
Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). However, high variance in thes…
cs.LG2026
Order Matters: Improving Domain Adaptation by Reordering Data
Andrea Napoli, Paul White
Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address this by minimising domain discrepa…