3 citations · 6 across the 10 of their papers we have counts for
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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…
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…
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…
Universal Online Learning with Unbounded Losses: Memory Is All You Need
Moise Blanchard, Romain Cosson, Steve Hanneke
We resolve an open problem of Hanneke on the subject of universally consistent online learning with non-i.i.d. processes and unbounded losses. The notion of an optimistically unive…