[17] Yurii Nesterov and Boris T Polyak. Cubic regularization of newton method and its global performance. Mathematical Programming, 108(1):177-205, 2006.
[18] Matthias Feurer and Frank Hutter. Hyperparameter optimization. Automated machine learning: Methods, systems, challenges, pages 3-33, 2019.
[19] Li Yang and Abdallah Shami. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415:295-316, 2020.
[20] Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, editors. Automated Machine Learning: Methods, Systems, Challenges. Springer, 2018.
[21] Benoit Colson, Patrice Marcotte, and Gilles Savard. An overview of bilevel optimization. Annals of Operations Research, 153(1):235-256, 2007.
[22] Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio. An empirical evaluation of deep architectures on problems with many factors of variation. In International Conference on Machine Learning, 2007.
[23] Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research, 18(1):6765-6816, 2017.
[24] Coralia Cartis, Nicholas IM Gould, and Philippe L Toint. Adaptive cubic regularisation methods for unconstrained optimization. part i: motivation, convergence and numerical results. Mathematical Programming, 127(2):245-295, 2011.
[25] Jonas Moritz Kohler and Aurelien Lucchi. Sub-sampled cubic regularization for non-convex optimization. In International Conference on Machine Learning, 2017.
[26] Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, and Michael I Jordan. Stochastic cubic regularization for fast nonconvex optimization. Advances in Neural Information Processing Systems, 2018.
[27] Zhe Wang, Yi Zhou, Yingbin Liang, and Guanghui Lan. Stochastic variance-reduced cubic regularization for nonconvex optimization. In International Conference on Artificial Intelligence and Statistics, 2019.
[28] Yongqi Zhang, Zhanke Zhou, Quanming Yao, and Yong Li. Kgtuner: Efficient hyper-parameter search for knowledge graph learning. arXiv preprint arXiv:2205.02460, 2022.
[29] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in Neural Information Processing Systems, 2018.
[30] Quanming Yao, Hansi Yang, Bo Han, Gang Niu, and James Kwok. Searching to exploit memorization effect in learning from corrupted labels. In International Conference on Machine Learning, 2020.
[31] Minhui Huang, Kaiyi Ji, Shiqian Ma, and Lifeng Lai. Efficiently escaping saddle points in bilevel optimization. arXiv preprint arXiv:2202.03684, 2022.
[32] Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157-1182, 2003.
[33] Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, and Yuval Kluger. Feature selection using stochastic gates. In International Conference on Machine Learning, 2020.
[34] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. In International Conference on Learning Representations, 2019.
[35] Shun-Ichi Amari. Natural gradient works efficiently in learning. Neural computation, 10(2):251-276, 1998.