ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity

2026-03-07

AI Summary

This work addresses the challenge of effectively controlling subgroup-specific conditional losses in multi-group learning. To this end, the authors propose ShakyPrepend, a novel multi-group learning framework inspired by differential privacy mechanisms. ShakyPrepend adaptively accounts for group structure and spatial heterogeneity, enabling precise control over losses across subgroups while significantly reducing sample complexity. Theoretical analysis demonstrates its superior sample efficiency compared to existing approaches. Empirical evaluations confirm that ShakyPrepend effectively enhances model fairness and generalization, with practical applicability in real-world deployment scenarios.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Privacy-enhancing technologies

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.

Problem

• multi-group learning
• conditional loss
• subgroups
• sample complexity
• spatial heterogeneity

Innovation

• multi-group learning
• differential privacy
• sample complexity
• ShakyPrepend
• spatial heterogeneity