Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lechner, Tosca"'
We present a framework for designing scores to summarize performance metrics. Our design has two multi-criteria objectives: (1) improving on scores should improve all performance metrics, and (2) achieving pareto-optimal scores should achieve pareto-
Externí odkaz:
http://arxiv.org/abs/2410.06290
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 (e.g., PAC learning of function classes), realizable learnability of a
Externí odkaz:
http://arxiv.org/abs/2406.17814
We initiate the study of computability requirements for adversarially robust learning. Adversarially robust PAC-type learnability is by now an established field of research. However, the effects of computability requirements in PAC-type frameworks ar
Externí odkaz:
http://arxiv.org/abs/2406.10161
Autor:
Lechner, Tosca, Ben-David, Shai
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 - no such parameter exists. Our techniques allow us to show similar r
Externí odkaz:
http://arxiv.org/abs/2304.08712
Autor:
Lechner, Tosca, Urner, Ruth
Strategic classification, i.e. classification under possible strategic manipulations of features, has received a lot of attention from both the machine learning and the game theory community. Most works focus on analysing properties of the optimal de
Externí odkaz:
http://arxiv.org/abs/2203.13421
With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations. The goal of such represent
Externí odkaz:
http://arxiv.org/abs/2107.03483