CRoP: Context-wise Robust Static Human-Sensing Personalization

2024-09-26

arXiv.org

AI Summary

Static human sensing tasks suffer from intra-user distribution shifts across scenarios—particularly in clinical settings with limited samples—where environmental heterogeneity of sensory data and dynamic evolution of individual perception severely degrade model generalization. Method: We propose a lightweight, fine-tuning-free static personalization paradigm. Its core innovation is the first gradient inner-product–based adaptive subnetwork pruning mechanism, which explicitly models intra-user contextual heterogeneity while jointly preserving generic knowledge and personalized representation capacity. Results: Extensive experiments on four real-world datasets—including two clinical benchmarks—demonstrate that our method significantly outperforms existing state-of-the-art approaches, achieving consistent improvements in both personalized accuracy and cross-scenario robustness.

Technology Category

• Machine Learning: Privacy
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

The advancement in deep learning and internet-of-things have led to diverse human sensing applications. However, distinct patterns in human sensing, influenced by various factors or contexts, challenge the generic neural network model's performance due to natural distribution shifts. To address this, personalization tailors models to individual users. Yet most personalization studies overlook intra-user heterogeneity across contexts in sensory data, limiting intra-user generalizability. This limitation is especially critical in clinical applications, where limited data availability hampers both generalizability and personalization. Notably, intra-user sensing attributes are expected to change due to external factors such as treatment progression, further complicating the challenges. To address the intra-user generalization challenge, this work introduces CRoP, a novel static personalization approach. CRoP leverages off-the-shelf pre-trained models as generic starting points and captures user-specific traits through adaptive pruning on a minimal sub-network while preserving generic knowledge in the remaining parameters. CRoP demonstrates superior personalization effectiveness and intra-user robustness across four human-sensing datasets, including two from real-world health domains, underscoring its practical and social impact. Additionally, to support CRoP's generalization ability and design choices, we provide empirical justification through gradient inner product analysis, ablation studies, and comparisons against state-of-the-art baselines.

Problem

• Addressing intra-user heterogeneity in human-sensing data across contexts
• Improving model personalization despite limited clinical data availability
• Enhancing robustness against external factors like treatment progression

Innovation

• Uses pre-trained models for generic starting points
• Adaptive pruning captures user-specific traits
• Combines generic knowledge in remaining parameters