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Talk

Robust and Fair Multisource Learning

  • Christoph Lampert (Institute of Science and Technology, Austria)
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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.

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seminar
25.04.24 16.05.24

Math Machine Learning seminar MPI MIS + UCLA

MPI for Mathematics in the Sciences Live Stream

Katharina Matschke

MPI for Mathematics in the Sciences Contact via Mail

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