From Non-Convex to Strongly Convex: Curvature-Adaptive FTPL for Online Optimization

arXiv:2606. 02948v1 Announce Type: new Abstract: Curvature adaptivity is a classical theme in online optimization: for convex Lipschitz losses, adaptive methods interpolate between the optimal $O(\sqrt{T})$ regret for general convex losses and $O(\log T)$ regret under strong convexity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.