Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and a Tight Univariate Rate
DGX agentarXiv:2608.17573v1 Announce Type: cross Abstract: In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambi