An efficient black-box reduction from PQ to TDS learning for any Boolean concept class in the distribution-free setting implies hardness for TDS learning of halfspaces, while membership queries enable efficient PQ learning of halfspaces via iterative Forster transforms.
Advances in neural information processing systems , volume=
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DPS quantifies deviation of per-sample decision patterns from class averages and shows linear correlation with generalization gaps while unifying degradation scenarios into a continuous trajectory.
MetaTrans improves unsupervised video domain adaptation performance by separating and subtracting spatial and temporal divergences via a dedicated module and a minimal two-term loss objective.
citing papers explorer
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Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
An efficient black-box reduction from PQ to TDS learning for any Boolean concept class in the distribution-free setting implies hardness for TDS learning of halfspaces, while membership queries enable efficient PQ learning of halfspaces via iterative Forster transforms.
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Understanding Generalization through Decision Pattern Shift
DPS quantifies deviation of per-sample decision patterns from class averages and shows linear correlation with generalization gaps while unifying degradation scenarios into a continuous trajectory.
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Return of Frustratingly Easy Unsupervised Video Domain Adaptation
MetaTrans improves unsupervised video domain adaptation performance by separating and subtracting spatial and temporal divergences via a dedicated module and a minimal two-term loss objective.