ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity

TL;DR

ShakyPrepend introduces a novel multi-group learning method that uses differential privacy techniques to enhance theoretical guarantees and adapt to subgroup structures and heterogeneity in practical applications.

Contribution

It presents ShakyPrepend, a new approach that improves sample complexity and theoretical guarantees in multi-group learning by leveraging differential privacy tools.

Findings

Demonstrates improved theoretical guarantees over existing methods

Shows adaptability to subgroup structures and spatial heterogeneity

Provides practical deployment guidance for real-world applications

Abstract

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.