2 citations · 2 across the 4 of their papers we have counts for
4 papers
On the Computability of Multiclass PAC Learning
Pascale Gourdeau, Tosca Lechner, Ruth Urner
We study the problem of computable multiclass learnability within the Probably Approximately Correct (PAC) learning framework of Valiant (1984). In the recently introduced computab…
Distribution Learnability and Robustness
Shai Ben-David, Alex Bie, Gautam Kamath +1
We examine the relationship between learnability and robust (or agnostic) learnability for the problem of distribution learning. We show that, contrary to other learning settings (…
On the Computability of Robust PAC Learning
Pascale Gourdeau, Tosca Lechner, Ruth Urner
We initiate the study of computability requirements for adversarially robust learning. Adversarially robust PAC-type learnability is by now an established field of research. Howeve…
Impossibility of Characterizing Distribution Learning -- a simple solution to a long-standing problem
Tosca Lechner, Shai Ben-David
We consider the long-standing question of finding a parameter of a class of probability distributions that characterizes its PAC learnability. We provide a rather surprising answer…