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.