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cs.LG2026
Uniform convergence at optimal rate and scale
Noga Alon, Steve Hanneke, Shay Moran +1
Uniform convergence asks how many random samples suffice for empirical averages to approximate expectations simultaneously over an entire class of functions. It is a central concep…
cs.LG2026
Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
Shashaank Aiyer, Yishay Mansour, Shay Moran +2
We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensitive generalization of the f…
cs.LG2026
Margin in Abstract Spaces
Yair Ashlagi, Roi Livni, Shay Moran +1
Margin-based learning, exemplified by linear and kernel methods, is one of the few classical settings where generalization guarantees are independent of the number of parameters. T…