PIVOT: Partially-observed Indexing for Variational Online adapTation

Domain adaptation (DA) in real-world applications often unfolds in an online fashion, where data arrives sequentially with limited domain access and imbalanced sampling across domains. For example, in personalized ads prediction, users from different demographic groups (e.g., countries or age cohorts) correspond to distinct domains with highly skewed data availability, while user interests evolve over time. However, existing domain adaptation methods typically assume full domain observability and balanced data access, limiting their applicability to real-world scenarios with online domain shift and data imbalance. To address these challenges, we propose Partially-observed Indexing for Variational Online adapTation (PIVOT), a continual domain adaptation framework designed for partial domain access and inter-domain sample imbalance. Starting from a base model pretrained on historical source and target domains, PIVOT incrementally updates latent domain indices over time using a smoothed reweighting kernel and a replay buffer to ensure stable adaptation. Experiments on both synthetic and real-world datasets demonstrate that PIVOT consistently outperforms state-of-the-art baselines in long-term accuracy under dynamic and resource-constrained conditions.