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cs.LG2026
Bandit PCA with Minimax Optimal Regret
Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa Saha
We study the bandit-feedback version of online principal component analysis (Bandit PCA): in each round , the adversary selects a symmetric gain matrix…
cs.LG2019★ 4 cited
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis Bach +1
We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictor…