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Poster
Robust Learning from Untrusted Sources
Nikola Konstantinov · Christoph H. Lampert

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #156
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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.

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

Author Information

Nikola Konstantinov (IST Austria)
Christoph H. Lampert (IST Austria)

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