ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
This work provides improved theoretical guarantees for multi-group learning, which is important for practitioners deploying fair and robust machine learning models.
This paper addresses multi-group learning, which aims to control predictors' conditional losses across subgroups. The authors propose ShakyPrepend, a method inspired by differential privacy, achieving improved theoretical guarantees compared to existing methods. Numerical experiments show its adaptability to group structure and spatial heterogeneity.
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose ShakyPrepend, a method that leverages tools inspired by differential privacy to obtain improved theoretical guarantees over existing approaches. Through numerical experiments, we demonstrate that ShakyPrepend adapts to both group structure and spatial heterogeneity. We provide practical guidance for deploying multi-group learning algorithms in real-world settings.