| 2026 | AAAI | FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models. | Junkang Liu, Fanhua Shang, Hongying Liu, Yuxuan Tian, Yuanyuan Liu, Jin Liu, Kewen Zhu, Zhouchen Lin |
| 2026 | ACL | FLASH: Focused Layer Attention Sink Hijacking. | Siyuan Deng, Yerong Li, Fanhua Shang, Hongying Liu |
| 2025 | AAAI | Unsupervised Degradation Representation Aware Transform for Real-World Blind Image Super-Resolution. | Sen Chen, Hongying Liu, Chaowei Fang, Fanhua Shang, Yuanyuan Liu, Liang Wan, Dongmei Jiang, Yaowei Wang |
| 2025 | CVPR | Dual Semantic Guidance for Open Vocabulary Semantic Segmentation. | Zhengyang Wang, Tingliang Feng, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan |
| 2025 | CVPR | Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation. | Hongmei Yin, Tingliang Feng, Fan Lyu, Fanhua Shang, Hongying Liu, Wei Feng, Liang Wan |
| 2025 | ICCV | Greg: GEometry-Aware RegIon Refinement for Sign Language Video Generation. | Tongkai Shi, Lianyu Hu, Fanhua Shang, Liqing Gao, Wei Feng |
| 2025 | ICCV | FedAGC: Federated Continual Learning with Asymmetric Gradient Correction. | Chengchao Zhang, Fanhua Shang, Hongyin Liu, Liang Wan, Wei Feng |
| 2025 | ICML | Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging. | Junkang Liu, Yuanyuan Liu, Fanhua Shang, Hongying Liu, Jin Liu, Wei Feng |
| 2024 | AAAI | SAVSR: Arbitrary-Scale Video Super-Resolution via a Learned Scale-Adaptive Network. | Zekun Li, Hongying Liu, Fanhua Shang, Yuanyuan Liu, Liang Wan, Wei Feng |
| 2024 | AAAI | Long-Tailed Learning as Multi-Objective Optimization. | Weiqi Li, Fan Lyu, Fanhua Shang, Liang Wan, Wei Feng |
| 2024 | ECCV | Pose-Guided Fine-Grained Sign Language Video Generation. | Tongkai Shi, Lianyu Hu, Fanhua Shang, Jichao Feng, Peidong Liu, Wei Feng |
| 2023 | ICASSP | Adaptive Non-Local Generative Adversarial Networks for Low-Dose CT Image Denoising. | Linlin Yang, Hongying Liu, Fanhua Shang, Yuanyuan Liu |
| 2023 | ICCV | Measuring Asymmetric Gradient Discrepancy in Parallel Continual Learning. | Fan Lyu, Qing Sun, Fanhua Shang, Liang Wan, Wei Feng |
| 2022 | AAAI | HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks. | Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu |
| 2022 | ICML | Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots. | Yuanyuan Liu, Fanhua Shang, Weixin An, Hongying Liu, Zhouchen Lin |
| 2022 | ICTAI | PWPROP: A Progressive Weighted Adaptive Method for Training Deep Neural Networks. | Dong Wang, Tao Xu, Huatian Zhang, Fanhua Shang, Hongying Liu, Yuanyuan Liu, Shengmei Shen |
| 2021 | AAAI | Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding. | Yangyang Li, Lin Kong, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Zhouchen Lin |
| 2021 | AAAI | Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling. | Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang, Yuanyuan Liu |
| 2021 | IJCAI | Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. | Hua Huang, Fanhua Shang, Yuanyuan Liu, Hongying Liu |
| 2021 | UAI | Principal component analysis in the stochastic differential privacy model. | Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu |
| 2020 | AAAI | Deep Residual-Dense Lattice Network for Speech Enhancement. | Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao, Jun Zhou, Kuldip K. Paliwal, Fanhua Shang |
| 2019 | AAAI | Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1, +1}. | Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao |
| 2019 | AISTATS | Direct Acceleration of SAGA using Sampled Negative Momentum. | Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo |
| 2019 | CIKM | Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning. | Xiangyang Liu, Bingkun Wei, Fanhua Shang, Hongying Liu |
| 2019 | ICDM | A Stochastic Variance Reduced Extragradient Method for Sparse Machine Learning Problems. | Lin Kong, Xiaying Bai, Yang Hu, Fanhua Shang, Yuanyuan Liu, Hongying Liu |
| 2019 | ICDM | A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images. | Hongying Liu, Zhongshu Wang, Fanhua Shang, Mingyang Zhang, Maoguo Gong, Feihang Ge, Licheng Jiao |
| 2019 | ICDM | Efficient Parallel Stochastic Variance Reduction Algorithms for Large-Scale SVD. | Fanhua Shang, Zhihui Zhang, Yuying An, Yang Hu, Hongying Liu |
| 2019 | ICDM | signADAM++: Learning Confidences for Deep Neural Networks. | Dong Wang, Yicheng Liu, Wenwo Tang, Fanhua Shang, Hongying Liu, Qigong Sun, Licheng Jiao |
| 2019 | IJCAI | Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor. | Yuanyuan Liu, Fanhua Shang, Licheng Jiao |
| 2019 | ICTAI | A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks. | Yuzhe Ma, Ran Chen, Wei Li, Fanhua Shang, Wenjian Yu, Minsik Cho, Bei Yu |
| 2019 | MICCAI | CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation. | Hongying Liu, Xiongjie Shen, Fanhua Shang, Feihang Ge, Fei Wang |
| 2018 | ACML | ASVRG: Accelerated Proximal SVRG. | Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin |
| 2018 | AISTATS | Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. | Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida |
| 2018 | ICML | A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. | Kaiwen Zhou, Fanhua Shang, James Cheng |
| 2017 | AAAI | Accelerated Variance Reduced Stochastic ADMM. | Yuanyuan Liu, Fanhua Shang, James Cheng |
| 2016 | AAAI | Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. | Fanhua Shang, Yuanyuan Liu, James Cheng |
| 2016 | AISTATS | Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. | Fanhua Shang, Yuanyuan Liu, James Cheng |
| 2014 | AAAI | Generalized Higher-Order Tensor Decomposition via Parallel ADMM. | Fanhua Shang, Yuanyuan Liu, James Cheng |
| 2014 | CIKM | Robust Principal Component Analysis with Missing Data. | Fanhua Shang, Yuanyuan Liu, James Cheng, Hong Cheng |
| 2014 | ICDM | Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization. | Fanhua Shang, Yuanyuan Liu, James Cheng, Hong Cheng |
| 2014 | UAI | Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds. | Yuanyuan Liu, Fanhua Shang, Hong Cheng, James Cheng |
| 2014 | SDM | Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion. | Yuanyuan Liu, Fanhua Shang, Hong Cheng, James Cheng, Hanghang Tong |
| 2012 | CIKM | Learning spectral embedding via iterative eigenvalue thresholding. | Fanhua Shang, Licheng Jiao, Yuanyuan Liu, Fei Wang |
| 2012 | KDD | Semi-supervised learning with mixed knowledge information. | Fanhua Shang, Licheng Jiao, Fei Wang |
| 2011 | ICDM | Learning Spectral Embedding for Semi-supervised Clustering. | Fanhua Shang, Yuanyuan Liu, Fei Wang |