CRoP: Context-wise Robust Static Human-Sensing Personalization

• URL: http://arxiv.org/abs/2409.17994v4
• Date: Tue, 19 Nov 2024 14:51:14 GMT
• Title: CRoP: Context-wise Robust Static Human-Sensing Personalization
• Authors: Sawinder Kaur, Avery Gump, Jingyu Xin, Yi Xiao, Harshit Sharma, Nina R Benway, Jonathan L Preston, Asif Salekin,
• Abstract summary: This work introduces CRoP, a novel static personalization approach. CRoP captures user-specific traits through adaptive pruning on a minimal sub-network while preserving generic knowledge in the remaining parameters. It demonstrates superior personalization effectiveness and intra-user robustness across four human-sensing datasets, including two from real-world health domains.
• Score: 7.303478793193849
• License:
• 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.

Related papers

• Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction [31.42047941275096]
  We propose a unique and practical approach that emphasizes historical ratings at both the user and product levels. We have developed an efficient data processing strategy that makes this approach highly scalable and easily deployable.
  arXiv  Detail & Related papers  (2024-09-06T23:16:06Z)
• Idiographic Personality Gaussian Process for Psychological Assessment [7.394943089551214]
  We develop a novel measurement framework based on a Gaussian process coregionalization model to address a long-lasting debate ins. We propose the idiographic personality Gaussian process (IPGP) framework, an intermediate model that accommodates both shared trait structure across a population and "idiographic" deviations for individuals.
  arXiv  Detail & Related papers  (2024-07-06T06:09:04Z)
• Causality and Independence Enhancement for Biased Node Classification [56.38828085943763]
  We propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs) Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations. Our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.
  arXiv  Detail & Related papers  (2023-10-14T13:56:24Z)
• Understanding Robust Overfitting from the Feature Generalization Perspective [61.770805867606796]
  Adversarial training (AT) constructs robust neural networks by incorporating adversarial perturbations into natural data. It is plagued by the issue of robust overfitting (RO), which severely damages the model's robustness. In this paper, we investigate RO from a novel feature generalization perspective.
  arXiv  Detail & Related papers  (2023-10-01T07:57:03Z)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• Improving Personality Consistency in Conversation by Persona Extending [22.124187337032946]
  We propose a novel retrieval-to-prediction paradigm consisting of two subcomponents, namely, Persona Retrieval Model (PRM) and Posterior-scored Transformer (PS-Transformer) Our proposed model yields considerable improvements in both automatic metrics and human evaluations.
  arXiv  Detail & Related papers  (2022-08-23T09:00:58Z)
• Partial sensitivity analysis in differential privacy [58.730520380312676]
  We investigate the impact of each input feature on the individual's privacy loss. We experimentally evaluate our approach on queries over private databases. We also explore our findings in the context of neural network training on synthetic data.
  arXiv  Detail & Related papers  (2021-09-22T08:29:16Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Model-agnostic Fits for Understanding Information Seeking Patterns in Humans [0.0]
  In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task. Here, we reexamine data from previous carefully designed experiments, collected at scale, that measured and catalogued these biases in aggregate form. We design deep learning models that replicate these biases in aggregate, while also capturing individual variation in behavior.
  arXiv  Detail & Related papers  (2020-12-09T04:34:58Z)
• ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes [64.87637128500889]
  Region of Interest Active Learning (ROIAL) framework actively learns each user's underlying utility function over a region of interest. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. Results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.
  arXiv  Detail & Related papers  (2020-11-09T22:45:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.