Robust supervised learning under uncertainty in dataset shift
When machine learning is deployed in the real world, its performance can be significantly undermined because test data may follow a different distribution from training data. To build a reliable machine learning system in such a scenario, we propose a supervised learning framework that is explicitly robust to the uncertainty of dataset shift. Our robust learning framework is flexible in modeling various dataset shift scenarios. It is also computationally efficient in that it acts as a robust wrapper around existing gradientbased supervised learning algorithms, while adding negligible computational overheads. We discuss practical considerations in robust supervised learning and show the effectiveness of our approach on both synthetic and benchmark datasets.
ArXiv