The field of domain generalization and adaptation is moving towards developing more robust and efficient algorithms that can handle complex distributions and domain shifts. Researchers are exploring new approaches to lift distribution-specific learners to more general ones, and to characterize the sample complexity of domain generalization. A key direction is the development of conditional feature alignment methods, which can preserve task-relevant variations while filtering out nuisance shifts. Another area of focus is the development of unsupervised domain adaptation methods, including dictionary learning approaches that can align distributions from different datasets. Notable papers include:

• A Distributional-Lifting Theorem for PAC Learning, which proves a distributional-lifting theorem that upgrades a learner to succeed with respect to any distribution.
• On the Theory of Conditional Feature Alignment for Unsupervised Domain-Adaptive Counting, which proposes a theoretical framework of conditional feature alignment for unsupervised domain-adaptive counting.