Robust Learning from Untrusted Sources
This addresses the challenge of data quality and privacy in machine learning when relying on external sources, though it appears incremental as it builds on existing statistical and optimization methods.
The paper tackles the problem of learning robustly from untrusted, distributed, or private data sources, and shows that their method significantly improves over existing approaches in robust statistics and distributed optimization.
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 external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional 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.