5 papers
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…
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…