Montag, 07. Dezember 2020, 16:45 - 17:45 iCal
ISOR Colloquium
! TALK MOVED TO SUMMER SEMESTER 2021 DUE TO COVID19 RESTRICTIONS @UNIVIE !
"Learning Robustly from Multiple Sources"
Speaker: Christoph H. Lampert (Institute of Science and Technology Austria)
HS 7 OMP1 (#1.303, 1st floor)
Oskar-Morgenstern-Platz 1, 1090 Wien
Vortrag
We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. Specifically, we analyze the situation in which a learning system obtains datasets from multiple sources, some of which might be biased or even adversarially perturbed. It is known that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent "learnability", that is, even in the limit of infinitely much training data, no learning system can approach the optimal test error. I present recent work with Nikola Konstantinov in which we show that, surprisingly, the same is not true in the multi-source setting, where the adversary can arbitrarily corrupt a fixed fraction of the data sources.
Zur Webseite der Veranstaltung
Veranstalter
Institut für Statistik und Operations Research
Kontakt
Sabine Sobotka-Tompits, BA
Fakultät für Wirtschaftswissenschaften
Institut für Statistik und Operations Research
+43 1 4277 38631
sabine.sobotka-tompits@univie.ac.at
Erstellt am Donnerstag, 26. November 2020, 12:03
Letzte Änderung am Dienstag, 15. Dezember 2020, 01:39