arXiv Machine Learning
Sharp Rates and a One-Line Correction for Spectral Representation Learning
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Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization
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Shortcuts in the Tail: Debiasing via Post-Hoc Spectral Compression of Fine-Tuning Updates
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Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
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Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences
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