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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.LG2026
Distribution-Free Sequential Prediction with Abstentions
Jialin Yu, Moïse Blanchard
We study a sequential prediction problem in which an adversary is allowed to inject arbitrarily many adversarial instances in a stream of i.i.d. instances, but at each round, the l…
stat.ML2026
Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness
Moïse Blanchard, Abhishek Shetty, Alexander Rakhlin
Understanding minimal assumptions that enable learning and generalization is perhaps the central question of learning theory. Several celebrated results in statistical learning the…