1 citations · 2 across the 6 of their papers we have counts for
6 papers
Simplifying Adversarially Robust PAC Learning with Tolerance
Hassan Ashtiani, Vinayak Pathak, Ruth Urner
Adversarially robust PAC learning has proved to be challenging, with the currently best known learners [Montasser et al., 2021a] relying on improper methods based on intricate comp…
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…
Calibration through the Lens of Interpretability
Alireza Torabian, Ruth Urner
Calibration is a frequently invoked concept when useful label probability estimates are required on top of classification accuracy. A calibrated model is a function whose values co…
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…
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…
Adversarially Robust Learning with Tolerance
Hassan Ashtiani, Vinayak Pathak, Ruth Urner
We initiate the study of tolerant adversarial PAC-learning with respect to metric perturbation sets. In adversarial PAC-learning, an adversary is allowed to replace a test point $x…