[1] M. Andrychowicz, M. Denil, S. Gómez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas. Learning to Learn by Gradient Descent by Gradient Descent. In Advances in Neural Information Processing Systems, 2016.
[2] Y. Bengio, S. Bengio, and J. Cloutier. Learning a Synaptic Learning Rule. Université de Montréal, Département d'informatique et de recherche opérationnelle, 1991.
[3] A. Cutkosky. Anytime Online-to-Batch, Optimism and Acceleration. In International Conference on Machine Learning, 2019.
[4] O. Dekel, A. Flajolet, N. Haghtalab, and P. Jaillet. Online learning with a hint. In Advances in Neural Information Processing Systems, 2017.
[5] J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(61):2121-2159, 2011.
[6] C. Finn, P. Abbeel, and S. Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In International Conference on Machine Learning, 2017.
[7] S. Flennerhag, P. G. Moreno, N. D. Lawrence, and A. Damianou. Transferring Knowledge across Learning Processes. In International Conference on Learning Representations, 2019.
[8] S. Flennerhag, Y. Schroecker, T. Zahavy, H. van Hasselt, D. Silver, and S. Singh. Bootstrapped Meta-Learning. arXiv preprint arXiv:2109.04504, 2021.
[9] S. Flennerhag, Y. Schroecker, T. Zahavy, H. van Hasselt, D. Silver, and S. Singh. Bootstrapped Meta-Learning. In International Conference on Learning Representations, 2022.
[10] G. E. Hinton and D. C. Plaut. Using Fast Weights to Deblur Old Memories. In Cognitive Science Society, 1987.
[11] S. Hochreiter, A. S. Younger, and P. R. Conwell. Learning To Learn Using Gradient Descent. In International Conference on Artificial Neural Networks, 2001.
[12] P. Joulani, A. György, and C. Szepesvári. A modular analysis of adaptive (non-) convex optimization: Optimism, composite objectives, and variational bounds. Journal of Machine Learning Research, 1:40, 2017.
[13] P. Joulani, A. Raj, A. Gyorgy, and C. Szepesvári. A Simpler Approach to Accelerated Optimization: Iterative Averaging Meets Optimism. In International Conference on Machine Learning, 2020.
[14] M. Khodak, M.-F. F. Balcan, and A. S. Talwalkar. Adaptive Gradient-Based Meta-Learning Methods. In Advances in Neural Information Processing Systems, 2019.
[15] L. Kirsch, S. van Steenkiste, and J. Schmidhuber. Improving Generalization in Meta Reinforcement Learning Using Learned Objectives. arXiv preprint arXiv:1910.04098, 2019.
[16] J. Luketina, S. Flennerhag, Y. Schroecker, D. Abel, T. Zahavy, and S. Singh. Meta-gradients in non-stationary environments. In ICLR Workshop on Agent Learning in Open-Endness, 2022.
[17] A. R. Mahmood, R. S. Sutton, T. Degris, and P. M. Pilarski. Tuning-Free Step-Size Adaptation. In ICASSP, 2012.
[18] H. B. McMahan. A survey of algorithms and analysis for adaptive online learning. The Journal of Machine Learning Research, 18(1):3117-3166, 2017.
[19] M. Mohri and S. Yang. Accelerating Online Convex Optimization via Adaptive Prediction. In International Conference on Artificial Intelligence and Statistics, 2016.
[20] Y. E. Nesterov. A method for solving the convex programming problem with convergence rate o  $(1 / \mathrm{k}^{\wedge}2)$ . In Dokl. akad. nauk Sssr, volume 269, pages 543-547, 1983.