1 citations · 1 across the 7 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
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…
cs.LG2024★ 1 cited
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…
cs.LG2024
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…