Robust Learning from Untrusted Sources

Abstract

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.

Accepted to International Conference on Machine Learning (ICML), 2019; Camera-ready version

Cited by in corpus (6)

• DivideMix: Learning with Noisy Labels as Semi-supervised Learning
• DistFL: Distribution-aware Federated Learning for Mobile Scenarios
• Aggregating From Multiple Target-Shifted Sources
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data
• Active Learning for Noisy Data Streams Using Weak and Strong Labelers
• Probabilistic Inference for Learning from Untrusted Sources