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This paper proposes the Label-Shift-Adjusted Bayesian Score for conformal prediction under label shift, demonstrating shorter prediction intervals with maintained coverage in molecular property prediction.
This paper addresses the challenges of domain adaptation in physics, where simulations differ from experimental data in nuisances and label shifts. It proposes adaptive domain adaptation to focus on genuine physical mismatches and provides a model selection rule.
This paper proposes OSPDIM, a source-free online unsupervised domain adaptation framework for EEG-based BCIs that corrects geometric misalignment caused by class-imbalanced label shifts on the Riemannian manifold, outperforming standard alignment methods in online scenarios.
This paper proposes a locality-aware private class identification approach and a reliable optimal transport-based method (ReOT) to address domain adaptation challenges under extreme label shift, particularly distinguishing shared from private classes.