[34] Yeqi Gao, Zhao Song, and Shenghao Xie. In-context learning for attention scheme: from single softmax regression to multiple softmax regression via a tensor trick. arXiv preprint arXiv:2307.02419, 2023.
[35] Yeqi Gao, Zhao Song, and Xin Yang. Differentially private attention computation. arXiv preprint arXiv:2305.04701, 2023.
[36] Yeqi Gao, Zhao Song, and Junze Yin. An iterative algorithm for rescaled hyperbolic functions regression. arXiv preprint arXiv:2305.00660, 2023.
[37] Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. Ppt: Pre-trained prompt tuning for few-shot learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410-8423, 2022.
[38] Yuzhou Gu and Zhao Song. A faster small treewidth sdp solver. arXiv preprint arXiv:2211.06033, 2022.
[39] Yuzhou Gu, Zhao Song, and Lichen Zhang. A nearly-linear time algorithm for structured support vector machines. arXiv preprint arXiv:2307.07735, 2023.
[40] Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. Warp: Word-level adversarial reprogramming. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4921-4933, 2021.
[41] Insu Han, Rajesh Jarayam, Amin Karbasi, Vahab Mirrokni, David P Woodruff, and Amir Zandieh. Hyperattention: Long-context attention in near-linear time. arXiv preprint arXiv:2310.05869, 2023.
[42] Keqing He, Jingang Wang, Chaobo Sun, and Wei Wu. Unified knowledge prompt pre-training for customer service dialogues. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 4009-4013, 2022.
[43] Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, et al. Hyperprompt: Prompt-based task-conditioning of transformers. In International Conference on Machine Learning, pages 8678-8690. PMLR, 2022.
[44] Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid O Seaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. arXiv preprint arXiv:1911.10422, 2019.
[45] Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp. In International Conference on Machine Learning, pages 2790-2799. PMLR, 2019.
[46] Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel. Vime: Variational information maximizing exploration. Advances in neural information processing systems, 29, 2016.
[47] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
[48] Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Juanzi Li, and Maosong Sun. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. arXiv preprint arXiv:2108.02035, 2021.
[49] Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao, and Ruizhe Zhang. Solving sdp faster: A robust ipm framework and efficient implementation. In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pages 233-244. IEEE, 2022.