[27] K. Ji and L. Ying. Network utility maximization with unknown utility functions: A distributed, data-driven bilevel optimization approach. arXiv preprint arXiv:2301.01801, 2023.
[28] S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh. Scaffolds: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132-5143. PMLR, 2020.
[29] A. Khaled, K. Mishchenko, and P. Richtárik. First analysis of local gd on heterogeneous data. arXiv preprint arXiv:1909.04715, 2019.
[30] P. Khanduri, S. Zeng, M. Hong, H.-T. Wai, Z. Wang, and Z. Yang. A near-optimal algorithm for stochastic bilevel optimization via double-momentum. Advances in neural information processing systems, 34:30271-30283, 2021.
[31] V. Konda and J. Tsitsiklis. Actor-critic algorithms. Advances in neural information processing systems, 12, 1999.
[32] J. Konečný, B. McMahan, and D. Ramage. Federated optimization: Distributed optimization beyond the datacenter. arXiv preprint arXiv:1511.03575, 2015.
[33] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[34] J. Li, B. Gu, and H. Huang. Improved bilevel model: Fast and optimal algorithm with theoretical guarantee. arXiv preprint arXiv:2009.00690, 2020.
[35] J. Li, F. Huang, and H. Huang. Local stochastic bilevel optimization with momentum-based variance reduction. arXiv preprint arXiv:2205.01608, 2022.
[36] J. Li, F. Huang, and H. Huang. Communication-efficient federated bilevel optimization with local and global lower level problems. arXiv preprint arXiv:2302.06701, 2023.
[37] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2:429-450, 2020.
[38] R. Liao, Y. Xiong, E. Fetaya, L. Zhang, K. Yoon, X. Pitkow, R. Urtasun, and R. Zemel. Reviving and improving recurrent back-propagation. In International Conference on Machine Learning, pages 3082-3091. PMLR, 2018.
[39] B. Liu, M. Ye, S. Wright, P. Stone, and Q. Liu. Bome! bilevel optimization made easy: A simple first-order approach. Advances in Neural Information Processing Systems, 35:17248-17262, 2022.
[40] R. Liu, J. Gao, J. Zhang, D. Meng, and Z. Lin. Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):10045-10067, 2021.
[41] R. Liu, X. Liu, X. Yuan, S. Zeng, and J. Zhang. A value-function-based interior-point method for non-convex bi-level optimization. In International Conference on Machine Learning, pages 6882–6892. PMLR, 2021.
[42] R. Liu, Y. Liu, S. Zeng, and J. Zhang. Towards gradient-based bilevel optimization with nonconvex followers and beyond. Advances in Neural Information Processing Systems, 34:8662-8675, 2021.
[43] R. Liu, P. Mu, X. Yuan, S. Zeng, and J. Zhang. A generic first-order algorithmic framework for bi-level programming beyond lower-level singleton. In International Conference on Machine Learning, pages 6305-6315. PMLR, 2020.
[44] S. Lu, X. Cui, M. S. Squillante, B. Kingsbury, and L. Horesh. Decentralized bilevel optimization for personalized client learning. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5543-5547. IEEE, 2022.