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Jing-Yee Tan

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Preprint Sep 2026

Sharp Regret Bounds and a Task-Covariance Correction for Spectral Representation Learning

This work derives alignment-dependent regret bounds, matching worst-case lower bounds for a flat leading spectrum, and bounds using the leading $2k$ directions with a spectral-tail term, and bound the imbalance from random preference patterns and from averaging independent tasks with an isotropic population covariance.

Di-Er Tang, Jing-Yee Tan, Guang-Yue Han · 1 citation
#machine learning Preprint Sep 2026

Sharp Rates and a One-Line Correction for Spectral Representation Learning

A self-supervised encoder is trained once, frozen, and reused through lightweight probes on tasks nobody named at training time; the practitioner's question is when the off-the-shelf features are good enough and when they need fixing. Canonical correlation analysis, HGR maximal correlation, and the population optimum o...

Di-Er Tang, Jing-Yee Tan, Guang-Yue Han · 0 citations

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