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Adaptive Gradient-Based Meta-Learning Methods

arXiv 1906.02717 ↗First posted 6 Jun 2019cs.LG cs.AI stat.MLcited by 183

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Abstract (arXiv)

We build a theoretical framework for designing and understanding practical meta-learning methods that integrates sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms. Our approach enables the task-similarity to be learned adaptively, provides sharper transfer-risk bounds in the setting of statistical learning-to-learn, and leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure. We use our theory to modify several popular meta-learning algorithms and improve their meta-test-time performance on standard problems in few-shot learning and federated learning.

Key facts. Cited by 183 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2019.

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Citing papers per year: 2020: 1; 2021: 48; 2022: 43; 2023: 42; 2024: 24; 2025: 15; 2026: 10.

183 papers.

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  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2021A Field Guide to Federated Optimizationcited by 312
  4. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  5. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  6. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  7. 2021On the Role of Neural Collapse in Transfer Learningcited by 100
  8. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  9. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  10. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  11. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  12. 2021Personalized Federated Learning through Local Memorizationcited by 57
38 more
  1. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  2. 2021Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learningcited by 52
  3. 2021Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptationcited by 50
  4. 2021Bootstrapped Meta-Learningcited by 45
  5. 2021Generalization Bounds For Meta-Learning: An Information-Theoretic Analysiscited by 45
  6. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 45
  7. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  8. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 43
  9. 2020Provable Representation Learning for Imitation Learning via Bi-level Optimizationcited by 41
  10. 2021How Fine-Tuning Allows for Effective Meta-Learningcited by 41
  11. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  12. 2023An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learningcited by 39
  13. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  14. 2021Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Taskscited by 36
  15. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  16. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  17. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  18. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  19. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  20. 2021On the Power of Multitask Representation Learning in Linear MDPcited by 30
  21. 2022Architecture Agnostic Federated Learning for Neural Networkscited by 30
  22. 2021Federated Mixture of Expertscited by 27
  23. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  24. 2023Federated Generalized Category Discoverycited by 26
  25. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  26. 2022Learning Predictions for Algorithms with Predictionscited by 25
  27. 2023Federated Domain Generalization: A Surveycited by 25
  28. 2022Wasserstein Task Embedding for Measuring Task Similaritiescited by 24
  29. 2021Meta-Adaptive Nonlinear Control: Theory and Algorithmscited by 23
  30. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  31. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  32. 2022Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learningcited by 22
  33. 2022Meta Knowledge Distillationcited by 20
  34. 2024Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Resetcited by 17
  35. 2021On Data Efficiency of Meta-learningcited by 16
  36. 2022Nearly Minimax Algorithms for Linear Bandits with Shared Representationcited by 16
  37. 2022Self-Aware Personalized Federated Learningcited by 16
  38. 2021Meta-Learning with Neural Tangent Kernelscited by 15

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