1 citations · 1 across the 5 of their papers we have counts for
7 papers
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…
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…
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…
Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
Bogdan Chornomaz, Yonatan Koren, Shay Moran +1
We study the problem of learning in the presence of an adversary that can corrupt an fraction of the training examples with the goal of causing failure on a specific test point…
Spherical dimension
Bogdan Chornomaz, Shay Moran, Tom Waknine
We introduce and study the spherical dimension, a natural topological relaxation of the VC dimension that unifies several results in learning theory where topology plays a key role…
On Reductions and Representations of Learning Problems in Euclidean Spaces
Bogdan Chornomaz, Shay Moran, Tom Waknine
Many practical prediction algorithms represent inputs in Euclidean space and replace the discrete 0/1 classification loss with a real-valued surrogate loss, effectively reducing cl…
List Sample Compression and Uniform Convergence
Steve Hanneke, Shay Moran, Tom Waknine
List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical princip…