Publications

Google Scholar

(* indicates equal contribution)

Preprints

[P2] Yihang Gao, Michael Ng, Michael W. Mahoney, Sen Na. Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming. Under review at a journal. arXiv

[P1] Yihang Gao, Vincent Y. F. Tan. Automatic Rank Determination for Low Rank Adaptation via Submodular Function Maximization. Short version accepted by MATH4AI Workshop @ AAAI 2026; Long version under review at a journal. arXiv

Journal Papers (Mathematics/ML)

[J6] Yihang Gao, Michael K. Ng, Vincent Y. F. Tan. Low Tensor-Rank Adaptation of Kolmogorov–Arnold Networks, IEEE Transactions on Signal Processing, 2025. paper

[J5] Yihang Gao, Vincent Y. F. Tan. On the Convergence of (Stochastic) Gradient Descent for Kolmogorov–Arnold Networks, IEEE Transactions on Information Theory, 2025. paper

[J4] Yihang Gao*, Chuanyang Zheng*, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael Ng, Zhenguo Li, Zhaoqiang Liu. AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures, Transactions on Machine Learning Research, 2025. paper

[J3] Yihang Gao, Michael K. Ng, Mingjie Zhou. Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks, SIAM Journal on Mathematics of Data Science, 2023. paper

[J2] Yihang Gao, Xuelei Lin, Michael K. Ng. Blind Deconvolution for Multiple Observed Images with Missing Values, Pacific Journal of Optimization, 2023. paper

[J1] Yihang Gao and Michael K. Ng. Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks, Journal of Computational Physics, 2022. paper (arXiv)

Conference Papers (Computer Science/ML/AI)

[C8] Chuanyang Zheng, Jiankai Sun, Yihang Gao, Yuehao Wang, Peihao Wang, Jing Xiong, Liliang Ren, Hao Cheng, Janardhan Kulkarni, Yelong Shen, Zhangyang Wang, Mac Schwager, Anderson Schneider, Xiaodong Liu, Jianfeng Gao, SAS: Simulated Attention Score, Advances in Neural Information Processing Systems (NeurIPS), 2025.

[C7] Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li. Self-Adjust Softmax, The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP Main), 2025.

[C6] Guoxuan Chen, Han Shi, Jiawei Li, Yihang Gao, Xiaozhe Ren, Yimeng Chen, Xin Jiang, Zhenguo Li, Weiyang Liu, Chao Huang. SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator, 42nd International Conference on Machine Learning (ICML), 2025.

[C5] Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li. DAPE V2: Process Attention Score as Feature Map for Length Extrapolation, The 63rd Annual Meeting of the Association for Computational Linguistics (ACL main), 2025.

[C4] Chuanyang Zheng*, Yihang Gao*, Han Shi, Minbin Huang, Jingyao Li, Jing Xiong, Xiaozhe Ren, Michael K. Ng, Xin Jiang, Zhenguo Li, Yu Li. DAPE: Data-Adaptive Positional Encoding for Length Extrapolation, Advances in Neural Information Processing Systems (NeurIPS), 2024. arXiv

[C3] Yihang Gao, Yiqi Gu, Michael K. Ng. Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks, 40th International Conference on Machine Learning (ICML), 2023. paper

[C2] Yihang Gao, Man-Chung Yue, Michael K. Ng. Approximate Secular Equations for the Cubic Regularization Subproblem, Advances in Neural Information Processing Systems (NeurIPS), 2022. paper / poster / slides

[C1] Yihang Gao, Ka Chun Cheung, Michael K. Ng. SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition, IEEE Symposium Series on Computational Intelligence 2022 (IEEE SSCI 2022). paper (arXiv)