[18] Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, and Massimiliano Pontil. Online-within-online meta-learning. Advances in Neural Information Processing Systems, 32, 2019.
[19] Yuri M Ermoliev and Vladimir I Norkin. Sample average approximation method for compound stochastic optimization problems. SIAM Journal on Optimization, 23(4):2231-2263, 2013.
[20] Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. Generalization of model-agnostic meta-learning algorithms: Recurring and unseen tasks. Advances in Neural Information Processing Systems, 34:5469–5480, 2021.
[21] Alec Farid and Anirudha Majumdar. Generalization bounds for meta-learning via PAC-bayes and uniform stability. Advances in Neural Information Processing Systems, 34:2173-2186, 2021.
[22] Anthony V Fiacco. Introduction to sensitivity and stability analysis in nonlinear programming. Academic press, 1983.
[23] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, pages 1126–1135. PMLR, 2017.
[24] Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine. Online meta-learning. In International Conference on Machine Learning, pages 1920-1930. PMLR, 2019.
[25] Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine. One-shot visual imitation learning via meta-learning. In Conference on Robot Learning, pages 357-368. PMLR, 2017.
[26] James Hannan. Approximation to bayes risk in repeated play. Contributions to the Theory of Games, 3(2):97-139, 1957.
[27] Nathan Hilliard, Lawrence Phillips, Scott Howland, Artem Yankov, Courtney D Corley, and Nathan O Hodes. Few-shot learning with metric-agnostic conditional embeddings. arXiv preprint arXiv:1802.04376, 2018.
[28] Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5149-5169, 2021.
[29] Yanlong Huang. Ekmp: Generalized imitation learning with adaptation, nonlinear hard constraints and obstacle avoidance. arXiv preprint arXiv:2103.00452, 2021.
[30] Yanlong Huang, Leonel Rozo, Joao Silvério, and Darwin G Caldwell. Kernelized movement primitives. The International Journal of Robotics Research, 38(7):833-852, 2019.
[31] Yu Huang, Yingbin Liang, and Longbo Huang. Provable generalization of overparameterized meta-learning trained with SGD. arXiv preprint arXiv:2206.09136, 2022.
[32] Kaiyi Ji, Junjie Yang, and Yingbin Liang. Bilevel optimization: Convergence analysis and enhanced design. In International Conference on Machine Learning, pages 4882-4892. PMLR, 2021.
[33] Kaiyi Ji, Junjie Yang, and Yingbin Liang. Theoretical convergence of multi-step model-agnostic meta-learning. Journal of Machine Learning Research, 23:29-1, 2022.
[34] Vanshaj Khattar, Yuhao Ding, Javad Lavaei, and Ming Jin. A CMDP-within-online framework for meta-safe reinforcement learning. In International Conference on Learning Representations, 2023.
[35] Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar. Adaptive gradient-based meta-learning methods. Advances in Neural Information Processing Systems, 32, 2019.
[36] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.