Yi Xu

According to our database, Yi Xu authored at least 59 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A contrastive semi-supervised remaining useful life prediction method with incomplete life histories on turbofan.

Tiancheng Wang, Yi Xu, Di Guo, Xi-Ming Sun

Comput. Electr. Eng., 2025

2024

Robust Semi-Supervised Learning by Wisely Leveraging Open-Set Data.

Yang Yang, Nan Jiang, Yi Xu, De-Chuan Zhan

IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples.

Peixuan Ding, Yi Xu, Pan Qin, Xi-Ming Sun

Appl. Intell., April, 2024

Multitask Learning for Aero-Engine Bearing Fault Diagnosis With Limited Data.

Peixuan Ding, Yi Xu, Xi-Ming Sun

IEEE Trans. Instrum. Meas., 2024

DBCTNet: Double Branch Convolution-Transformer Network for Hyperspectral Image Classification.

Rui Xu, Xue-Mei Dong, Weijie Li, Jiangtao Peng, Weiwei Sun, Yi Xu

IEEE Trans. Geosci. Remote. Sens., 2024

Multi-agent cooperative strategy with explicit teammate modeling and targeted informative communication.

Rui Jiang, Xuetao Zhang, Yisha Liu, Yi Xu, Xuebo Zhang, Yan Zhuang

Neurocomputing, 2024

Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace.

Shian Du, Xiaotian Cheng, Qi Qian, Henglu Wei, Yi Xu, Xiangyang Ji

CoRR, 2024

The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge.

Hongpeng Pan, Shifeng Yi, Shouwei Yang, Lei Qi, Bing Hu, Yi Xu, Yang Yang

CoRR, 2024

Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks.

Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu, Jinghui Tang

CoRR, 2024

Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023.

Hongpeng Pan, Yang Yang, Zhongtian Fu, Yuxuan Zhang, Shian Du, Yi Xu, Xiangyang Ji

CoRR, 2024

The Solution for the CVPR 2023 1st foundation model challenge-Track2.

Haonan Xu, Yurui Huang, Sishun Pan, Zhihao Guan, Yi Xu, Yang Yang

CoRR, 2024

Facilitating Multimodal Classification via Dynamically Learning Modality Gap.

Yang Yang, Fengqiang Wan, Qing-Yuan Jiang, Yi Xu

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

RSNN: Recurrent Spiking Neural Networks for Dynamic Spatial-Temporal Information Processing.

Qi Xu, Xuanye Fang, Yaxin Li, Jiangrong Shen, De Ma, Yi Xu, Gang Pan

Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks.

Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency.

Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Changxin Gao, Rong Jin, Nong Sang

IEEE Trans. Pattern Anal. Mach. Intell., October, 2023

Attentional-Biased Stochastic Gradient Descent.

Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

Trans. Mach. Learn. Res., 2023

Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning.

Yang Yang, Yuxuan Zhang, Xin Song, Yi Xu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Supported Value Regularization for Offline Reinforcement Learning.

Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Supported Trust Region Optimization for Offline Reinforcement Learning.

Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji

Proceedings of the International Conference on Machine Learning, 2023

In-sample Actor Critic for Offline Reinforcement Learning.

Hongchang Zhang, Yixiu Mao, Boyuan Wang, Shuncheng He, Yi Xu, Xiangyang Ji

Proceedings of the Eleventh International Conference on Learning Representations, 2023

TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization.

Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Fairness via Adversarial Attribute Neighbourhood Robust Learning.

Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang

CoRR, 2022

An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation.

Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin, Xiangyang Ji, Antoni B. Chan

CoRR, 2022

Improved Fine-Tuning by Better Leveraging Pre-Training Data.

Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni B. Chan, Rong Jin

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Effective Model Sparsification by Scheduled Grow-and-Prune Methods.

Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency.

Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

CHEX: CHannel EXploration for CNN Model Compression.

Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

A Novel Convergence Analysis for Algorithms of the Adam Family.

Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

CoRR, 2021

Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice.

Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Antoni B. Chan, Rong Jin

CoRR, 2021

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification.

Hao Luo, Pichao Wang, Yi Xu, Feng Ding, Yanxin Zhou, Fan Wang, Hao Li, Rong Jin

CoRR, 2021

Why Does Multi-Epoch Training Help?

Yi Xu, Qi Qian, Hao Li, Rong Jin

CoRR, 2021

On Stochastic Moving-Average Estimators for Non-Convex Optimization.

Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

CoRR, 2021

A Theoretical Analysis of Learning with Noisily Labeled Data.

Yi Xu, Qi Qian, Hao Li, Rong Jin

CoRR, 2021

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity.

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

CoRR, 2021

On the Convergence of Deep Networks with Sample Quadratic Overparameterization.

Asaf Noy, Yi Xu, Yonathan Aflalo, Rong Jin

CoRR, 2021

An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives.

Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity.

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang

Proceedings of the 38th International Conference on Machine Learning, 2021

Dash: Semi-Supervised Learning with Dynamic Thresholding.

Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, Rong Jin

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Attentional Biased Stochastic Gradient for Imbalanced Classification.

Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang

CoRR, 2020

WeMix: How to Better Utilize Data Augmentation.

Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin

CoRR, 2020

Towards Understanding Label Smoothing.

Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin

CoRR, 2020

A Practical Online Method for Distributionally Deep Robust Optimization.

Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang

CoRR, 2020

Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

CoRR, 2020

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Optimization for Non-convex Inf-Projection Problems.

Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure.

Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang

CoRR, 2019

Learning with Non-Convex Truncated Losses by SGD.

Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems.

Yi Xu, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization.

Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence.

Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang

Proceedings of the 36th International Conference on Machine Learning, 2019

Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number.

Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time.

Yi Xu, Rong Jin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods.

Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter.

Yi Xu, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization.

Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence.

Yi Xu, Qihang Lin, Tianbao Yang

Proceedings of the 34th International Conference on Machine Learning, 2017

Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee.

Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound.

Yi Xu, Qihang Lin, Tianbao Yang

CoRR, 2016

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon).

Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016