mlsanityData to 7 Oct 2026

Personalization of Deep Learning

arXiv 1909.02803 ↗First posted 6 Sep 2019cs.LG stat.MLcited by 18

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Abstract (arXiv)

We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. We show that both ``curriculuum learning'' and ``personalized'' data augmentation lead to improved performance on data of an individual. Mostly, this comes at the cost of reduced performance on a more general, broader dataset.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2019.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 3; 2024: 5; 2025: 3; 2026: 1.

18 papers.

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  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  3. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  4. 2024Exploring Safety-Utility Trade-Offs in Personalized Language Modelscited by 26
  5. 2023The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillationcited by 25
  6. 2024Test-Time Personalization with Meta Prompt for Gaze Estimationcited by 8
  7. 2022Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answerscited by 6
  8. 2022Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learningcited by 4
  9. 2023Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interactioncited by 4
  10. 2024FedSheafHN: Personalized Federated Learning on Graph-structured Datacited by 2
  11. 2024GazeGen: Gaze-Driven User Interaction for Visual Content Generationcited by 1
  12. 2025Personalized Subgraph Federated Learning with Sheaf Collaborationcited by 1
6 more
  1. 2025Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distributioncited by 1
  2. 2021Targeted Deep Learning: Framework, Methods, and Applicationscited by 0
  3. 2022A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challengescited by 0
  4. 2024Improving Personalisation in Valence and Arousal Prediction using Data Augmentationcited by 0
  5. 2025Adaptive Dual-Mode Distillation with Incentive Schemes for Scalable, Heterogeneous Federated Learning on Non-IID Datacited by 0
  6. 2026Representation-Aligned Multi-Scale Personalization for Federated Learningcited by 0