A semi-automatic editing method for surgery videos. - Zhiwei Fan; Congliang Chen; Tingting Jiang 001 (ICME Workshops, 2017)

Arbitrary Style Transfer With Deep Feature Reshuffle. - Shuyang Gu; Congliang Chen; Jing Liao 001; Lu Yuan (CVPR, 2018)

Quantized Adam with Error Feedback. - Congliang Chen; Li Shen 005; Haozhi Huang 001; Wei Liu 005 (CoRR, 2020)

Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. - Congliang Chen; Jiawei Zhang 007; Li Shen 005; Peilin Zhao; Zhi-Quan Luo (AISTATS, 2021)

Quantized Adam with Error Feedback. - Congliang Chen; Li Shen 008; Hao-Zhi Huang 001; Wei Liu 005 (ACM Trans. Intell. Syst. Technol., 2021)

Adam Can Converge Without Any Modification On Update Rules. - Yushun Zhang; Congliang Chen; Naichen Shi; Ruoyu Sun 001; Zhi-Quan Luo (NeurIPS, 2022)

Efficient-Adam: Communication-Efficient Distributed Adam with Complexity Analysis. - Congliang Chen; Li Shen 005; Wei Liu 005; Zhi-Quan Luo (CoRR, 2022)

Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration. - Congliang Chen; Li Shen 005; Fangyu Zou; Wei Liu 005 (J. Mach. Learn. Res., 2022)

Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz. - Tao Sun 005; Congliang Chen; Peng Qiao; Li Shen 008; Xinwang Liu 002; Dongsheng Li 001 (CoRR, 2023)

Efficient-Adam: Communication-Efficient Distributed Adam. - Congliang Chen; Li Shen 008; Wei Liu 005; Zhi-Quan Luo (IEEE Trans. Signal Process., 2023)

Unlocking Black-Box Prompt Tuning Efficiency via Zeroth-Order Optimization. - Heshen Zhan; Congliang Chen; Tian Ding; Ziniu Li; Ruoyu Sun 001 (EMNLP (Findings), 2024)

Why Transformers Need Adam: A Hessian Perspective. - Yushun Zhang; Congliang Chen; Tian Ding; Ziniu Li; Ruoyu Sun 001; Zhi-Quan Luo (NeurIPS, 2024)

Adam-mini: Use Fewer Learning Rates To Gain More. - Yushun Zhang; Congliang Chen; Ziniu Li; Tian Ding; Chenwei Wu 002; Yinyu Ye 001; Zhi-Quan Luo; Ruoyu Sun 001 (CoRR, 2024)

Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity. - Ziniu Li; Congliang Chen; Tian Xu 003; Zeyu Qin; Jiancong Xiao; Ruoyu Sun 001; Zhi-Quan Luo (CoRR, 2024)

Exploring the Generalization Capabilities of AID-based Bi-level Optimization. - Congliang Chen; Li Shen 008; Zhiqiang Xu 003; Wei Liu 005; Zhi-Quan Luo; Peilin Zhao (CoRR, 2024)

A Unified Analysis of AdaGrad With Weighted Aggregation and Momentum Acceleration. - Li Shen 008; Congliang Chen; Fangyu Zou; Zequn Jie; Ju Sun; Wei Liu 005 (IEEE Trans. Neural Networks Learn. Syst., 2024)

Preserving Diversity in Supervised Fine-Tuning of Large Language Models. - Ziniu Li; Congliang Chen; Tian Xu 003; Zeyu Qin; Jiancong Xiao; Zhi-Quan Luo; Ruoyu Sun 001 (ICLR, 2025)

Adam-mini: Use Fewer Learning Rates To Gain More. - Yushun Zhang; Congliang Chen; Ziniu Li; Tian Ding; Chenwei Wu 002; Diederik P. Kingma; Yinyu Ye 001; Zhi-Quan Luo; Ruoyu Sun 001 (ICLR, 2025)

AdaGC: Improving Training Stability for Large Language Model Pretraining. - Guoxia Wang; Shuai Li; Congliang Chen; Jinle Zeng; Jiabin Yang; Tao Sun; Yanjun Ma; Dianhai Yu; Li Shen (CoRR, 2025)

Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives. - Yajiao Liu; Congliang Chen; Junchi Yang; Ruoyu Sun 001 (CoRR, 2025)