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ORCIDYi Xu 0008
Person information
- affiliation: Dalian University of Technology, School of Artificial Intelligence, Dalian, China
- affiliation: Alibaba DAMO Academy
- affiliation (PhD 2019): University of Iowa, Department of Computer Science, Iowa City, IA, USA
Other persons with the same name
- Yi Xu — disambiguation page
- Yi Xu 0001
![[0000-0001-6508-4469]](/proxy/https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-6508-4469")
— Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, China - Yi Xu 0002 — InnoPeak Technology, Inc., OPPO US Research Center, Palo Alto, CA, USA (and 1 more)
- Yi Xu 0003 — Fudan University, Shanghai, China
- Yi Xu 0004 — Shanghai Jiao Tong University, China
- Yi Xu 0005 — Northeastern University, Boston, MA, USA (and 1 more)
- Yi Xu 0006 — University of North Carolina, Chapel Hill, USA
- Yi Xu 0007 — University College London, Department of Speech, Hearing and Phonetic Sciences, United Kingdom
- Yi Xu 0010 — Macau University of Science and Technology, State Key Laboratory of Lunar and Planetary Sciences, Macau (and 1 more)
- Yi Xu 0011 — Amazon, Seattle, WA, USA (and 1 more)
- Yi Xu 0012 — North Carolina State University, Department of Electrical and Computer Engineering, Raleigh, NC, USA
- Yi Xu 0013 — Beihang University, School of Computer Science and Engineering, Institute of Artificial Intelligence, State Key Laboratory of Software Development Environment, Beijing, China
- Yi Xu 0014 — Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore (and 1 more)
- Yi Xu 0015 — Anhui University, School of Computer Science and Technology, Key Laboratory of Intelligent Computing and Signal Processing, Hefei, China
- Yi Xu 0016 — Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, China (and 1 more)
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2020 – today
- 2024
[j6]Peixuan Ding, Yi Xu, Pan Qin, Xi-Ming Sun:
A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples. Appl. Intell. 54(7): 5306-5316 (2024)- [j5]Rui Jiang, Xuetao Zhang, Yisha Liu, Yi Xu, Xuebo Zhang, Yan Zhuang:
Multi-agent cooperative strategy with explicit teammate modeling and targeted informative communication. Neurocomputing 586: 127638 (2024) - [j4]Rui Xu, Xue-Mei Dong, Weijie Li, Jiangtao Peng, Weiwei Sun, Yi Xu:
DBCTNet: Double Branch Convolution-Transformer Network for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote. Sens. 62: 1-15 (2024) - [j3]Peixuan Ding, Yi Xu, Xi-Ming Sun:
Multitask Learning for Aero-Engine Bearing Fault Diagnosis With Limited Data. IEEE Trans. Instrum. Meas. 73: 1-11 (2024) - [c27]Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan:
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. ICML 2024 - [i33]Haonan Xu, Yurui Huang, Sishun Pan, Zhihao Guan, Yi Xu, Yang Yang:
The Solution for the CVPR 2023 1st foundation model challenge-Track2. CoRR abs/2403.17702 (2024) - [i32]Hongpeng Pan, Yang Yang, Zhongtian Fu, Yuxuan Zhang, Shian Du, Yi Xu, Xiangyang Ji:
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023. CoRR abs/2403.17994 (2024) - [i31]Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu, Jinghui Tang:
Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks. CoRR abs/2404.08347 (2024) - [i30]Yang Yang, Nan Jiang, Yi Xu, De-Chuan Zhan:
Robust Semi-supervised Learning by Wisely Leveraging Open-set Data. CoRR abs/2405.06979 (2024) - [i29]Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan:
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. CoRR abs/2405.08886 (2024) - [i28]Hongpeng Pan, Shifeng Yi, Shouwei Yang, Lei Qi, Bing Hu, Yi Xu, Yang Yang:
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge. CoRR abs/2406.12225 (2024) - [i27]Shian Du, Xiaotian Cheng, Qi Qian, Henglu Wei, Yi Xu, Xiangyang Ji:
Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace. CoRR abs/2407.00608 (2024) - 2023
- [j2]Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Changxin Gao, Rong Jin, Nong Sang:
Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency. IEEE Trans. Pattern Anal. Mach. Intell. 45(10): 12408-12426 (2023) - [j1]Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
Attentional-Biased Stochastic Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023) - [c26]Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan:
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. CVPR 2023: 16436-16446 - [c25]Hongchang Zhang, Yixiu Mao, Boyuan Wang, Shuncheng He, Yi Xu, Xiangyang Ji:
In-sample Actor Critic for Offline Reinforcement Learning. ICLR 2023 - [c24]Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji:
Supported Trust Region Optimization for Offline Reinforcement Learning. ICML 2023: 23829-23851 - [c23]Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji:
Supported Value Regularization for Offline Reinforcement Learning. NeurIPS 2023 - [c22]Yang Yang, Yuxuan Zhang, Xin Song, Yi Xu:
Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning. NeurIPS 2023 - [i26]Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan:
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. CoRR abs/2303.11135 (2023) - [i25]Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji:
Supported Trust Region Optimization for Offline Reinforcement Learning. CoRR abs/2311.08935 (2023) - 2022
- [c21]Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung:
CHEX: CHannel EXploration for CNN Model Compression. CVPR 2022: 12277-12288 - [c20]Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang:
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency. CVPR 2022: 13811-13821 - [c19]Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie:
Effective Model Sparsification by Scheduled Grow-and-Prune Methods. ICLR 2022 - [c18]Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni B. Chan, Rong Jin:
Improved Fine-Tuning by Better Leveraging Pre-Training Data. NeurIPS 2022 - [i24]Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung:
CHEX: CHannel EXploration for CNN Model Compression. CoRR abs/2203.15794 (2022) - [i23]Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang:
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency. CoRR abs/2204.03017 (2022) - [i22]Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin, Xiangyang Ji, Antoni B. Chan:
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation. CoRR abs/2205.12753 (2022) - [i21]Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang:
Fairness via Adversarial Attribute Neighbourhood Robust Learning. CoRR abs/2210.06630 (2022) - 2021
- [c17]Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, Rong Jin:
Dash: Semi-Supervised Learning with Dynamic Thresholding. ICML 2021: 11525-11536 - [c16]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML 2021: 12219-12229 - [c15]Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang:
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS 2021: 10067-10080 - [i20]Asaf Noy, Yi Xu, Yonathan Aflalo, Rong Jin:
On the Convergence of Deep Networks with Sample Quadratic Overparameterization. CoRR abs/2101.04243 (2021) - [i19]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR abs/2102.04635 (2021) - [i18]Yi Xu, Qi Qian, Hao Li, Rong Jin:
A Theoretical Analysis of Learning with Noisily Labeled Data. CoRR abs/2104.04114 (2021) - [i17]Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
On Stochastic Moving-Average Estimators for Non-Convex Optimization. CoRR abs/2104.14840 (2021) - [i16]Yi Xu, Qi Qian, Hao Li, Rong Jin:
Why Does Multi-Epoch Training Help? CoRR abs/2105.06015 (2021) - [i15]Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie:
Effective Model Sparsification by Scheduled Grow-and-Prune Methods. CoRR abs/2106.09857 (2021) - [i14]Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin:
Dash: Semi-Supervised Learning with Dynamic Thresholding. CoRR abs/2109.00650 (2021) - [i13]Hao Luo, Pichao Wang, Yi Xu, Feng Ding, Yanxin Zhou, Fan Wang, Hao Li, Rong Jin:
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification. CoRR abs/2111.12084 (2021) - [i12]Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Antoni B. Chan, Rong Jin:
Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice. CoRR abs/2111.12292 (2021) - [i11]Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
A Novel Convergence Analysis for Algorithms of the Adam Family. CoRR abs/2112.03459 (2021) - 2020
- [c14]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. ICML 2020: 10660-10669 - [c13]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS 2020 - [i10]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR abs/2002.05309 (2020) - [i9]Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang:
A Practical Online Method for Distributionally Deep Robust Optimization. CoRR abs/2006.10138 (2020) - [i8]Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin:
Towards Understanding Label Smoothing. CoRR abs/2006.11653 (2020) - [i7]Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin:
WeMix: How to Better Utilize Data Augmentation. CoRR abs/2010.01267 (2020) - [i6]Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang:
Attentional Biased Stochastic Gradient for Imbalanced Classification. CoRR abs/2012.06951 (2020)
2010 – 2019
- 2019
- [c12]Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang:
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. ICML 2019: 1102-1111 - [c11]Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang:
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML 2019: 6942-6951 - [c10]Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang:
On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. IJCAI 2019: 4003-4009 - [c9]Yi Xu, Rong Jin, Tianbao Yang:
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS 2019: 2626-2636 - [c8]Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. UAI 2019: 701-711 - [i5]Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang:
Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR abs/1904.10112 (2019) - [i4]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. CoRR abs/1908.09941 (2019) - 2018
- [c7]Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang:
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. ICML 2018: 912-920 - [c6]Yi Xu, Rong Jin, Tianbao Yang:
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. NeurIPS 2018: 5535-5545 - [i3]Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. CoRR abs/1805.07880 (2018) - 2017
- [c5]Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang:
Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI 2017: 2796-2802 - [c4]Yi Xu, Qihang Lin, Tianbao Yang:
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML 2017: 3821-3830 - [c3]Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS 2017: 1267-1277 - [c2]Yi Xu, Qihang Lin, Tianbao Yang:
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS 2017: 3277-3287 - 2016
- [c1]Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang:
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS 2016: 1208-1216 - [i2]Yi Xu, Qihang Lin, Tianbao Yang:
Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR abs/1607.01027 (2016) - [i1]Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang:
Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. CoRR abs/1612.01663 (2016)
Coauthor Index
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