5 citations · 7 across the 9 of their papers we have counts for
9 papers
Discriminative Feature Feedback with General Teacher Classes
Omri Bar Oz, Tosca Lechner, Sivan Sabato
We study the theoretical properties of the interactive learning protocol Discriminative Feature Feedback (DFF) (Dasgupta et al., 2018). The DFF learning protocol uses feedback in t…
On the Learnability of Distribution Classes with Adaptive Adversaries
Tosca Lechner, Alex Bie, Gautam Kamath
We consider the question of learnability of distribution classes in the presence of adaptive adversaries -- that is, adversaries capable of intercepting the samples requested by a…
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…
Score Design for Multi-Criteria Incentivization
Anmol Kabra, Mina Karzand, Tosca Lechner +2
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 m…
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…
Learning Losses for Strategic Classification
Tosca Lechner, Ruth Urner
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…
Impossibility results for fair representations
Tosca Lechner, Shai Ben-David, Sushant Agarwal +1
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 intere…