Abstract for the talk on 14.04.2022 (17:00 h)

Math Machine Learning seminar MPI MIS + UCLA

Christoph Lampert (Institute of Science and Technology, Austria)
Robust and Fair Multisource Learning
See the video of this talk.

In the era of big data, the training data for

machine learning models is commonly collected from multiple

sources. Some of these might not be unreliable (noisy,

corrupted, or even manipulated). Can learning algorithms

overcome this an still learn classifiers of optimal accuracy

and ideally fairness? In my talk, I highlight recent results

from our group that establish situations in which this is

possible or impossible.


22.04.2022, 07:48