Papers
1–3 of 3Research Paper·Mar 18, 2026
Revisiting Cross-Attention Mechanisms: Leveraging Beneficial Noise for Domain-Adaptive Learning
Unsupervised Domain Adaptation (UDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain but often suffers from severe domain and scale gaps that degrade performance...
7.0 viability
Research Paper·Mar 16, 2026
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes. Existing proxy-based ap...
4.0 viability
Research Paper·Jan 29, 2026
Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) is a statistical learning problem when the distribution of training (source) data is different from that of test (target) data. In this setting, one has access to ...
3.0 viability