[20] Zhouyuan Huo, Bin Gu, Ji Liu, and Heng Huang. Accelerated method for stochastic composition optimization with nonsmooth regularization. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
[21] Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh. Mime: Mimicking centralized stochastic algorithms in federated learning. arXiv preprint arXiv:2008.03606, 2020.
[22] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International conference on machine learning, pages 5132-5143. PMLR, 2020.
[23] Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, and Pramod Varshney. Stem: A stochastic two-sided momentum algorithm achieving near-optimal sample and communication complexities for federated learning. Advances in Neural Information Processing Systems, 34: 6050-6061, 2021.
[24] Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum. One shot learning of simple visual concepts. In Proceedings of the annual meeting of the cognitive science society, volume 33, 2011.
[25] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2:429-450, 2020.
[26] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273-1282. PMLR, 2017.
[27] Youssef Mroueh, Stephen Voinea, and Tomaso A Poggio. Learning with group invariant features: A kernel perspective. Advances in neural information processing systems, 28, 2015.
[28] Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, and Tianbao Yang. Stochastic optimization of areas under precision-recall curves with provable convergence. Advances in Neural Information Processing Systems, 34:1752-1765, 2021.
[29] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H Brendan McMahan. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020.
[30] Rahul Singh, Maneesh Sahani, and Arthur Gretton. Kernel instrumental variable regression. Advances in Neural Information Processing Systems, 32, 2019.
[31] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016.
[32] Bokun Wang and Tianbao Yang. Finite-sum coupled compositional stochastic optimization: Theory and applications. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 23292-23317. PMLR, 2022.
[33] Bokun Wang, Zhuoning Yuan, Yiming Ying, and Tianbao Yang. Memory-based optimization methods for model-agnostic meta-learning. arXiv preprint arXiv:2106.04911, 2021.
[34] Guanghui Wang, Ming Yang, Lijun Zhang, and Tianbao Yang. Momentum accelerates the convergence of stochastic auprc maximization. In International Conference on Artificial Intelligence and Statistics, pages 3753-3771. PMLR, 2022.
[35] Mengdi Wang, Ji Liu, and Ethan Fang. Accelerating stochastic composition optimization. Advances in Neural Information Processing Systems, 29, 2016.
[36] Mengdi Wang, Ethan X Fang, and Han Liu. Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions. Mathematical Programming, 161(1):419-449, 2017.
[37] Xidong Wu, Feihu Huang, and Heng Huang. Fast stochastic recursive momentum methods for imbalanced data mining. In 2022 IEEE International Conference on Data Mining (ICDM), pages 578-587. IEEE, 2022.
[38] Xidong Wu, Zhengmian Hu, Jian Pei, and Heng Huang. Serverless federated uprc optimization for multi-party collaborative imbalanced data mining. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2648-2659, 2023.