Montag, 12. Juni 2023, 16:45 - 17:45 iCal

ISOR Colloquium

"Learning Robustly from Multiple Sources"

Speaker: Christoph Lampert (Institute of Science and Technology Austria, Austria)

HS 7 OMP1 (#1.303)
Oskar-Morgenstern-Platz 1, 1090 Wien


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.

Underlying paper:


The talk also can be joined online via our ZOOM MEETING:

Meeting room opens at: June 12, 2023, 4.30 pm Vienna

Meeting ID: 662 5111 2914

Password: 013246

Zur Webseite der Veranstaltung


Institut für Statistik und Operations Research


Sabine Sobotka-Tompits, BA
Fakultät für Wirtschaftswissenschaften
Institut für Statistik und Operations Research
+43 1 4277 38631