arXiv Machine Learning
Sep 14

Block-Norm Geometries for Online Mirror Descent with Sparse Losses

The paper investigates how the choice of geometry in online mirror descent affects performance, particularly when loss gradients are sparse. It introduces randomized block‑norm mirror maps that interpolate between Euclidean and entropic geometries, achieving polynomial‑in‑dimension regret improvements over standard methods for various convex sets. The authors also demonstrate that naive alternation between mirror maps can lead to linear regret and propose a Hedge‑based meta‑algorithm that competes with the best mirror map in a finite portfolio, achieving near‑optimal regret for random block geometries.

By Swati Gupta, Jai Moondra, Mohit Singh
arXiv Machine Learning
Aug 18

Online Convex Optimization with Dueling Feedback

arXiv:2608. 15050v1 Announce Type: new Abstract: We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried points.

By Yiyang Lu, Hareshkumar Jadav, Mohammad Pedramfar, Ranveer Singh, Vaneet Aggarwal