12 citations · 19 across the 7 of their papers we have counts for
7 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…
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…
Sample Complexity of Robust Learning against Evasion Attacks
Pascale Gourdeau
It is becoming increasingly important to understand the vulnerability of machine learning models to adversarial attacks. One of the fundamental problems in adversarial machine lear…
When are Local Queries Useful for Robust Learning?
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska +1
Distributional assumptions have been shown to be necessary for the robust learnability of concept classes when considering the exact-in-the-ball robust risk and access to random ex…
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska +1
A fundamental problem in adversarial machine learning is to quantify how much training data is needed in the presence of evasion attacks. In this paper we address this issue within…
On the Hardness of Robust Classification
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska +1
It is becoming increasingly important to understand the vulnerability of machine learning models to adversarial attacks. In this paper we study the feasibility of robust learning f…
Bisimulation Metrics for Weighted Automata
Borja Balle, Pascale Gourdeau, Prakash Panangaden
We develop a new bisimulation (pseudo)metric for weighted finite automata (WFA) that generalizes Boreale's linear bisimulation relation. Our metrics are induced by seminorms on the…