[33] Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, and Decebal Constantin Mocanu. Deep ensembling with no overhead for either training or testing: The all-round blessings of dynamic sparsity. arXiv preprint arXiv:2106.14568, 2021.
[34] Zhuoqun Liu, Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, and Hongkai Xiong. Adaptive task sampling and variance reduction for gradient-based meta-learning. 2022.
[35] Ricardo Luna Gutierrez and Matteo Leonetti. Information-theoretic task selection for meta-reinforcement learning. Advances in Neural Information Processing Systems, 33:20532-20542, 2020.
[36] Eric J Martin, Valery R Polyakov, Xiang-Wei Zhu, Li Tian, Prasenjit Mukherjee, and Xin Liu. All-assay-max2 pqsar: activity predictions as accurate as four-concentration ic50s for 8558 novartis assays. Journal of chemical information and modeling, 59(10):4450–4459, 2019.
[37] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1406-1415, 2019.
[38] Yunzhe Qi, Yikun Ban, and Jingrui He. Neural bandit with arm group graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1379-1389, 2022.
[39] Yunzhe Qi, Yikun Ban, and Jingrui He. Graph neural bandits. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1920–1931, 2023.
[40] Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals. Rapid learning or feature reuse? towards understanding the effectiveness of maml. In International Conference on Learning Representations, 2019.
[41] Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, and Matthieu Cord. Diverse weight averaging for out-of-distribution generalization. arXiv preprint arXiv:2205.09739, 2022.
[42] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. 2016.
[43] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. Learning to reweight examples for robust deep learning. In International conference on machine learning, pages 4334-4343. PMLR, 2018.
[44] Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd international conference on world wide web, pages 373-374, 2014.
[45] Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. Meta-weight-net: Learning an explicit mapping for sample weighting. Advances in neural information processing systems, 32, 2019.
[46] Michal Valko, Nathaniel Korda, Rémi Munos, Ilias Flaounas, and Nelo Cristianini. Finite-time analysis of kernelised contextual bandits. arXiv preprint arXiv:1309.6869, 2013.
[47] Haoxiang Wang, Ruoyu Sun, and Bo Li. Global convergence and generalization bound of gradient-based meta-learning with deep neural nets. arXiv preprint arXiv:2006.14606, 2020.
[48] Haoxiang Wang, Yite Wang, Ruoyu Sun, and Bo Li. Global convergence of maml and theory-inspired neural architecture search for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9797-9808, June 2022.
[49] Qingyun Wu, Huazheng Wang, Quanquan Gu, and Hongning Wang. Contextual bandits in a collaborative environment. In SIGIR, pages 529-538, 2016.