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Journal Articles
- 2024
[j12]Haigen Min
, Xiaoping Lei, Xia Wu, Yukun Fang, Shixiang Chen, Wuqi Wang, Xiangmo Zhao:
Toward interpretable anomaly detection for autonomous vehicles with denoising variational transformer. Eng. Appl. Artif. Intell. 129: 107601 (2024)- [j11]Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao:
AdaSAM: Boosting sharpness-aware minimization with adaptive learning rate and momentum for training deep neural networks. Neural Networks 169: 506-519 (2024) - [j10]Shixiang Chen, Shiqian Ma, Anthony Man-Cho So, Tong Zhang:
Nonsmooth Optimization over the Stiefel Manifold and Beyond: Proximal Gradient Method and Recent Variants. SIAM Rev. 66(2): 319-352 (2024) - [j9]Wei Zhou, Zhijie Lyu, Shixiang Chen:
Mechanisms Influencing the Digital Transformation Performance of Local Governments: Evidence from China. Syst. 12(1): 30 (2024) - [j8]Shixiang Chen, Alfredo García, Mingyi Hong, Shahin Shahrampour:
On the Local Linear Rate of Consensus on the Stiefel Manifold. IEEE Trans. Autom. Control. 69(4): 2324-2339 (2024) - 2023
- [j7]Haigen Min, Yukun Fang, Xia Wu, Xiaoping Lei, Shixiang Chen, Rui Teixeira, Bing Zhu, Xiangmo Zhao, Zhigang Xu:
A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis. Expert Syst. Appl. 224: 120002 (2023) - 2022
- [j6]Zhongruo Wang, Bingyuan Liu, Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hongyu Zhao:
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis. INFORMS J. Optim. 4(2): 200-214 (2022) - [j5]Shixiang Chen, Alfredo García, Shahin Shahrampour:
On Distributed Nonconvex Optimization: Projected Subgradient Method for Weakly Convex Problems in Networks. IEEE Trans. Autom. Control. 67(2): 662-675 (2022) - 2021
- [j4]Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man-Cho So:
Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods. SIAM J. Optim. 31(3): 1605-1634 (2021) - [j3]Shixiang Chen, Zengde Deng, Shiqian Ma, Anthony Man-Cho So:
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning. IEEE Trans. Signal Process. 69: 4759-4773 (2021) - 2020
- [j2]Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hui Zou:
An Alternating Manifold Proximal Gradient Method for Sparse Principal Component Analysis and Sparse Canonical Correlation Analysis. INFORMS J. Optim. 2(3): 192-208 (2020) - [j1]Shixiang Chen, Shiqian Ma, Anthony Man-Cho So, Tong Zhang:
Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold. SIAM J. Optim. 30(1): 210-239 (2020)
Conference and Workshop Papers
- 2024
- [c8]Jinxin Wang, Jiang Hu, Shixiang Chen, Zengde Deng, Anthony Man-Cho So:
Decentralized Non-Smooth Optimization Over the Stiefel Manifold. SAM 2024: 1-5 - [c7]Shixiang Chen, Haigen Min, Yukun Fang, Xia Wu, Baolu Li, Xiangmo Zhao:
Uncertainty-aware Sensor Data Anomaly Detection for Autonomous Vehicles. IV 2024: 478-483 - 2023
- [c6]Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao:
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. ICML 2023: 32991-33013 - 2022
- [c5]Linrui Zhang, Li Shen, Long Yang, Shixiang Chen, Xueqian Wang, Bo Yuan, Dacheng Tao:
Penalized Proximal Policy Optimization for Safe Reinforcement Learning. IJCAI 2022: 3744-3750 - [c4]Yibo Yang, Shixiang Chen, Xiangtai Li, Liang Xie, Zhouchen Lin, Dacheng Tao:
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? NeurIPS 2022 - 2021
- [c3]Shixiang Chen, Alfredo García, Mingyi Hong, Shahin Shahrampour:
Decentralized Riemannian Gradient Descent on the Stiefel Manifold. ICML 2021: 1594-1605 - 2019
- [c2]Shixiang Chen, Zengde Deng, Shiqian Ma, Anthony Man-Cho So:
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning. ACSSC 2019: 259-263 - 2017
- [c1]Shixiang Chen, Shiqian Ma, Wei Liu:
Geometric Descent Method for Convex Composite Minimization. NIPS 2017: 636-644
Informal and Other Publications
- 2024
- [i16]Youbang Sun, Shixiang Chen, Alfredo García, Shahin Shahrampour:
Global Convergence of Decentralized Retraction-Free Optimization on the Stiefel Manifold. CoRR abs/2405.11590 (2024) - 2023
- [i15]Chao Xue, Wei Liu, Shuai Xie, Zhenfang Wang, Jiaxing Li, Xuyang Peng, Liang Ding, Shanshan Zhao, Qiong Cao, Yibo Yang, Fengxiang He, Bohua Cai, Rongcheng Bian, Yiyan Zhao, Heliang Zheng, Xiangyang Liu, Dongkai Liu, Daqing Liu, Li Shen, Chang Li, Shijin Zhang, Yukang Zhang, Guanpu Chen, Shixiang Chen, Yibing Zhan, Jing Zhang, Chaoyue Wang, Dacheng Tao:
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System. CoRR abs/2303.00501 (2023) - [i14]Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao:
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks. CoRR abs/2303.00565 (2023) - [i13]Jinxin Wang, Jiang Hu, Shixiang Chen, Zengde Deng, Anthony Man-Cho So:
Decentralized Weakly Convex Optimization Over the Stiefel Manifold. CoRR abs/2303.17779 (2023) - [i12]Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao:
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. CoRR abs/2305.11584 (2023) - [i11]Hao Sun, Li Shen, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao:
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data. CoRR abs/2309.09719 (2023) - 2022
- [i10]Yibo Yang, Liang Xie, Shixiang Chen, Xiangtai Li, Zhouchen Lin, Dacheng Tao:
Do We Really Need a Learnable Classifier at the End of Deep Neural Network? CoRR abs/2203.09081 (2022) - [i9]Linrui Zhang, Li Shen, Long Yang, Shixiang Chen, Bo Yuan, Xueqian Wang, Dacheng Tao:
Penalized Proximal Policy Optimization for Safe Reinforcement Learning. CoRR abs/2205.11814 (2022) - 2021
- [i8]Shixiang Chen, Alfredo García, Mingyi Hong, Shahin Shahrampour:
On the Local Linear Rate of Consensus on the Stiefel Manifold. CoRR abs/2101.09346 (2021) - [i7]Shixiang Chen, Alfredo García, Mingyi Hong, Shahin Shahrampour:
Decentralized Riemannian Gradient Descent on the Stiefel Manifold. CoRR abs/2102.07091 (2021) - 2020
- [i6]Shixiang Chen, Alfredo García, Shahin Shahrampour:
Distributed Projected Subgradient Method for Weakly Convex Optimization. CoRR abs/2004.13233 (2020) - [i5]Shixiang Chen, Zengde Deng, Shiqian Ma, Anthony Man-Cho So:
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning. CoRR abs/2005.02356 (2020) - [i4]Zhongruo Wang, Bingyuan Liu, Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hongyu Zhao:
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis. CoRR abs/2007.09524 (2020) - 2019
- [i3]Shixiang Chen, Shiqian Ma, Lingzhou Xue, Hui Zou:
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA. CoRR abs/1903.11576 (2019) - [i2]Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man-Cho So:
Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods. CoRR abs/1911.05047 (2019) - 2016
- [i1]Shixiang Chen, Shiqian Ma:
Geometric descent method for convex composite minimization. CoRR abs/1612.09034 (2016)
Coauthor Index
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