6 papers
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…
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…
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…
Variance Matters: Improving Domain Adaptation via Stratified Sampling
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…
Clustering-Based Validation Splits for Model Selection under Domain Shift
Andrea Napoli, Paul White
This paper considers the problem of model selection under domain shift. Motivated by principles from distributionally robust optimisation and domain adaptation theory, it is propos…
Improving Distribution Alignment with Diversity-based Sampling
Andrea Napoli, Paul White
Domain shifts are ubiquitous in machine learning, and can substantially degrade a model's performance when deployed to real-world data. To address this, distribution alignment meth…