$\left[\mathrm{FHW}^{+}17\right]$  Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel. Reverse curriculum generation for reinforcement learning. In Conference on robot learning, pages 482-495. PMLR, 2017.
[GBM+17] Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu. Automated curriculum learning for neural networks. In international conference on machine learning, pages 1311-1320. PMLR, 2017.
$\left[\mathrm{GHZ}^{+}18\right]$  Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang. Curriculumnet: Weakly supervised learning from large-scale web images. In Proceedings of the European conference on computer vision (ECCV), pages 135-150, 2018.
[HW19] Guy Hacohen and Daphna Weinshall. On the power of curriculum learning in training deep networks. In International Conference on Machine Learning, pages 2535–2544. PMLR, 2019.
[JMMH14] Lu Jiang, Deyu Meng, Teruko Mitamura, and Alexander G Hauptmann. Easy samples first: Self-paced reranking for zero-example multimedia search. In Proceedings of the 22nd ACM international conference on Multimedia, pages 547-556, 2014.
[KB14] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[KB17] Tom Kocmi and Ondrej Bojar. Curriculum learning and minibatch bucketing in neural machine translation. arXiv preprint arXiv:1707.09533, 2017.
[Kea98] Michael Kearns. Efficient noise-tolerant learning from statistical queries. Journal of the ACM (JACM), 45(6):983-1006, 1998.
[KPK10] M Kumar, Benjamin Packer, and Daphne Koller. Self-paced learning for latent variable models. Advances in neural information processing systems, 23, 2010.
[MKAS21] Eran Malach, Pritish Kamath, Emmanuel Abbe, and Nathan Srebro. Quantifying the benefit of using differentiable learning over tangent kernels. In International Conference on Machine Learning, pages 7379-7389. PMLR, 2021.
[MMN18] Song Mei, Andreaa Montanari, and Phan-Minh Nguyen. A mean field view of the landscape of two-layer neural networks. Proceedings of the National Academy of Sciences, 115(33):E7665-E7671, 2018.
[MSS20] Eran Malach and Shai Shalev-Shwartz. Computational separation between convolutional and fully-connected networks. arXiv preprint arXiv:2010.01369, 2020.
[NPL+20] Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E Taylor, and Peter Stone. Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research, 21(1):7382-7431, 2020.
$\left[\mathrm{PGM}^{+}19\right]$  Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
[PSL15] Anastasia Pentina, Viktoria Sharmanska, and Christoph H Lampert. Curriculum learning of multiple tasks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5492-5500, 2015.
[PSN+19] Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom M Mitchell. Competence-based curriculum learning for neural machine translation. arXiv preprint arXiv:1903.09848, 2019.
[RK90] Brian H Ross and Patrick T Kennedy. Generalizing from the use of earlier examples in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(1):42, 1990.