Skip to yearly menu bar Skip to main content


Robust Learning from Untrusted Sources

Nikola Konstantinov · Christoph H. Lampert

Pacific Ballroom #156

Keywords: [ Statistical Learning Theory ] [ Robust Statistics and Machine Learning ] [ Large Scale Learning and Big Data ] [ Crowdsourcing ]


Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from multiple external sources, \eg via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As further complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization.

Live content is unavailable. Log in and register to view live content