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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…
stat.ML2025
Distributionally-Constrained Adversaries in Online Learning
Moïse Blanchard, Samory Kpotufe
There has been much recent interest in understanding the continuum from adversarial to stochastic settings in online learning, with various frameworks including smoothed settings p…
stat.ML2025
Agnostic Smoothed Online Learning without Knowledge of the Base Measure
Moïse Blanchard
Classical results in statistical learning typically consider two extreme data-generating models: i.i.d. instances from an unknown distribution, or fully adversarial instances, ofte…