Talk
Robust and Fair Multisource Learning
- Christoph Lampert (Institute of Science and Technology, Austria)
Abstract
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.
