[54] Michael Celentano, Chen Cheng, and Andrea Montanari. The high-dimensional asymptotics of first order methods with random data. arXiv preprint arXiv:2112.07572, 2021.
[55] James B Simon, Madeline Dickens, Dhruva Karkada, and Michael Deweese. The eigenlearning framework: A conservation law perspective on kernel ridge regression and wide neural networks. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=FDbQGCAViI.
[56] Maria Refinetti, Alessandro Ingrosso, and Sebastian Goldt. Neural networks trained with sgd learn distributions of increasing complexity. In International Conference on Machine Learning, pages 28843-28863. PMLR, 2023.
[57] Alessandro Ingrosso and Sebastian Goldt. Data-driven emergence of convolutional structure in neural networks. Proceedings of the National Academy of Sciences, 119(40):e2201854119, 2022.
[58] Aditya Vardhan Varre, Loucas Pillaud-Vivien, and Nicolas Flammarion. Last iterate convergence of sgd for least-squares in the interpolation regime. Advances in Neural Information Processing Systems, 34:21581-21591, 2021.
[59] Maksim Velikanov, Denis Kuznedev, and Dmitry Yarotsky. A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=bzaPGE11sje.
[60] John O'Keefe. Place units in the hippocampus of the freely moving rat. Experimental neurology, 51(1):78-109, 1976.
[61] Edvard I Moser, Emilio Kropff, and May-Britt Moser. Place cells, grid cells, and the brain's spatial representation system. Annu. Rev. Neurosci., 31:69-89, 2008.
[62] Robert A Jacobs. Increased rates of convergence through learning rate adaptation. Neural networks, 1(4):295-307, 1988.
[63] William Dabney and Andrew Barto. Adaptive step-size for online temporal difference learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 26, pages 872-878, 2012.
[64] Andrew Y Ng, Daishi Harada, and Stuart J Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning, pages 278-287, 1999.
[65] Burrhus Frederic Skinner. Science and human behavior. Number 92904. Simon and Schuster, 1965.
[66] Vijaykumar Gullapalli and Andrew G Barto. Shaping as a method for accelerating reinforcement learning. In Proceedings of the 1992 IEEE international symposium on intelligent control, pages 554-559. IEEE, 1992.
[67] Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In Proceedings of the 26th annual international conference on machine learning, pages 41-48, 2009.
[68] Lenaic Chizat, Edouard Oyallon, and Francis Bach. On lazy training in differentiable programming. Advances in neural information processing systems, 32, 2019.
[69] Arthur Jacot, Franck Gabriel, and Clement Hongler. Neural tangent kernel: Convergence and generalization in neural networks. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31, pages 8571-8580. Curran Associates, Inc., 2018. URL https://proceedings.neurips.cc/paper/2018/file/5a4be1fa34e62bb8a6ec6b91d2462f5a-Paper.pdf.
[70] Hado Van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, and Joseph Modayil. Deep reinforcement learning and the deadly triad. arXiv preprint arXiv:1812.02648, 2018.