1 citations · 1 across the 4 of their papers we have counts for
4 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…
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…
Unsupervised Domain Adaptation Via Data Pruning
Andrea Napoli, Paul White
The removal of carefully-selected examples from training data has recently emerged as an effective way of improving the robustness of machine learning models. However, the best way…