Yi Xu - RankMe

91 papers A* 32A 1Journal 53Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds. AAAI Xuesong Jia, Yuanjie Shi, Ziquan Liu, Yi Xu, Yan Yan
2026	A*	`conf`	Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory. AAAI Huiyan Xue, Xuming Ran, Yaxin Li, Qi Xu, Enhui Li, Yi Xu, Qiang Zhang
2026	A*	`conf`	Score-Based Model for Low-Rank Tensor Recovery. AAAI Zhengyun Cheng, Changhao Wang, Guanwen Zhang, Yi Xu, Wei Zhou, Xiangyang Ji
2025	J	`jnl`	A contrastive semi-supervised remaining useful life prediction method with incomplete life histories on turbofan. Comput. Electr. Eng. Tiancheng Wang, Yi Xu, Di Guo, Xi-Ming Sun
2025	A*	`conf`	Advanced SpikingYOLOX: Extending Spiking Neural Network on Object Detection with Spike-based Partial Self-Attention and 2D-Spiking Transformer. ACM Multimedia Wei Miao, Jiangrong Shen, Hongming Xu, Tommi Kärkkäinen, Qi Xu, Yi Xu, Fengyu Cong
2025	A*	`conf`	BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations. AAAI Yusong Wang, Xuanye Fang, Huifeng Yin, Dongyuan Li, Guoqi Li, Qi Xu, Yi Xu, Shuai Zhong, Mingkun Xu
2025	J	`jnl`	ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction. CoRR Danhui Chen, Ziquan Liu, Chuxi Yang, Dan Wang, Yan Yan, Yi Xu, Xiangyang Ji
2025	J	`jnl`	Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds. CoRR Xuesong Jia, Yuanjie Shi, Ziquan Liu, Yi Xu, Yan Yan
2025	J	`jnl`	Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory. CoRR Huiyan Xue, Xuming Ran, Yaxin Li, Qi Xu, Enhui Li, Yi Xu, Qiang Zhang
2025	A*	`conf`	Efficient ANN-SNN Conversion with Error Compensation Learning. ICML Chang Liu, Jiangrong Shen, Xuming Ran, Mingkun Xu, Qi Xu, Yi Xu, Gang Pan
2025	J	`jnl`	Efficient ANN-SNN Conversion with Error Compensation Learning. CoRR Chang Liu, Jiangrong Shen, Xuming Ran, Mingkun Xu, Qi Xu, Yi Xu, Gang Pan
2025	J	`jnl`	GVI: Guideable Visual Interpretation on medical tomographic images to improve the performance of deep network. Pattern Recognit. Lett. Hui Liu, Fan Wei, Lixin Yan, Sushan Wang, Chongfu Jia, Lina Zhang, Jiansheng Peng, Yi Xu
2025	J	`jnl`	Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks. IEEE Trans. Pattern Anal. Mach. Intell. Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu, Jinhui Tang
2025	J	`jnl`	Low-Rank Implicit Neural Representation via Schatten-p Quasi-Norm and Jacobian Regularization. CoRR Zhengyun Cheng, Changhao Wang, Guanwen Zhang, Yi Xu, Wei Zhou, Xiangyang Ji
2025	—	`conf`	Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation. KDD (1) Qi (Cheems) Wang, Yiqin Lv, Yixiu Mao, Yun Qu, Yi Xu, Xiangyang Ji
2025	J	`jnl`	Score-Based Model for Low-Rank Tensor Recovery. CoRR Zhengyun Cheng, Changhao Wang, Guanwen Zhang, Yi Xu, Wei Zhou, Xiangyang Ji
2025	A*	`conf`	SpikingYOLOX: Improved YOLOX Object Detection with Fast Fourier Convolution and Spiking Neural Networks. AAAI Wei Miao, Jiangrong Shen, Qi Xu, Timo Hämäläinen, Yi Xu, Fengyu Cong
2025	J	`jnl`	Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises. CoRR Tianxi Zhu, Yi Xu, Xiangyang Ji
2025	J	`jnl`	Unified convergence analysis for adaptive optimization with moving average estimator. Mach. Learn. Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2024	J	`jnl`	A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples. Appl. Intell. Peixuan Ding, Yi Xu, Pan Qin, Xi-Ming Sun
2024	J	`jnl`	DBCTNet: Double Branch Convolution-Transformer Network for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote. Sens. Rui Xu, Xue-Mei Dong, Weijie Li, Jiangtao Peng, Weiwei Sun, Yi Xu
2024	J	`jnl`	Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace. CoRR Shian Du, Xiaotian Cheng, Qi Qian, Henglu Wei, Yi Xu, Xiangyang Ji
2024	A*	`conf`	Facilitating Multimodal Classification via Dynamically Learning Modality Gap. NeurIPS Yang Yang, Fengqiang Wan, Qing-Yuan Jiang, Yi Xu
2024	J	`jnl`	Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks. CoRR Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu, Jinghui Tang
2024	J	`jnl`	Multi-agent cooperative strategy with explicit teammate modeling and targeted informative communication. Neurocomputing Rui Jiang, Xuetao Zhang, Yisha Liu, Yi Xu, Xuebo Zhang, Yan Zhuang
2024	J	`jnl`	Multitask Learning for Aero-Engine Bearing Fault Diagnosis With Limited Data. IEEE Trans. Instrum. Meas. Peixuan Ding, Yi Xu, Xi-Ming Sun
2024	—	`conf`	ParCo: Part-Coordinating Text-to-Motion Synthesis. ECCV (56) Qiran Zou, Shangyuan Yuan, Shian Du, Yu Wang, Chang Liu, Yi Xu, Jie Chen, Xiangyang Ji
2024	J	`jnl`	ParCo: Part-Coordinating Text-to-Motion Synthesis. CoRR Qiran Zou, Shangyuan Yuan, Shian Du, Yu Wang, Chang Liu, Yi Xu, Jie Chen, Xiangyang Ji
2024	A*	`conf`	RSNN: Recurrent Spiking Neural Networks for Dynamic Spatial-Temporal Information Processing. ACM Multimedia Qi Xu, Xuanye Fang, Yaxin Li, Jiangrong Shen, De Ma, Yi Xu, Gang Pan
2024	J	`jnl`	Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation. CoRR Qi (Cheems) Wang, Yiqin Lv, Yixiu Mao, Yun Qu, Yi Xu, Xiangyang Ji
2024	J	`jnl`	Robust Semi-Supervised Learning by Wisely Leveraging Open-Set Data. IEEE Trans. Pattern Anal. Mach. Intell. Yang Yang, Nan Jiang Jiang, Yi Xu, De-Chuan Zhan
2024	J	`jnl`	Robust Semi-supervised Learning by Wisely Leveraging Open-set Data. CoRR Yang Yang, Nan Jiang Jiang, Yi Xu, De-Chuan Zhan
2024	J	`jnl`	Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023. CoRR Hongpeng Pan, Yang Yang, Zhongtian Fu, Yuxuan Zhang, Shian Du, Yi Xu, Xiangyang Ji
2024	A*	`conf`	The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. ICML Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan
2024	J	`jnl`	The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. CoRR Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan
2024	J	`jnl`	The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge. CoRR Hongpeng Pan, Shifeng Yi, Shouwei Yang, Lei Qi, Bing Hu, Yi Xu, Yang Yang
2024	J	`jnl`	The Solution for the CVPR 2023 1st foundation model challenge-Track2. CoRR Haonan Xu, Yurui Huang, Sishun Pan, Zhihao Guan, Yi Xu, Yang Yang
2023	J	`jnl`	Attentional-Biased Stochastic Gradient Descent. Trans. Mach. Learn. Res. Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2023	A*	`conf`	In-sample Actor Critic for Offline Reinforcement Learning. ICLR Hongchang Zhang, Yixiu Mao, Boyuan Wang, Shuncheng He, Yi Xu, Xiangyang Ji
2023	A*	`conf`	Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning. NeurIPS Yang Yang, Yuxuan Zhang, Xin Song, Yi Xu
2023	J	`jnl`	Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency. IEEE Trans. Pattern Anal. Mach. Intell. Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Changxin Gao, Rong Jin, Nong Sang
2023	A*	`conf`	Supported Trust Region Optimization for Offline Reinforcement Learning. ICML Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji
2023	J	`jnl`	Supported Trust Region Optimization for Offline Reinforcement Learning. CoRR Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji
2023	A*	`conf`	Supported Value Regularization for Offline Reinforcement Learning. NeurIPS Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji
2023	A*	`conf`	TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. CVPR Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan
2023	J	`jnl`	TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. CoRR Ziquan Liu, Yi Xu, Xiangyang Ji, Antoni B. Chan
2022	J	`jnl`	An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation. CoRR Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin, Xiangyang Ji, Antoni B. Chan
2022	A*	`conf`	CHEX: CHannel EXploration for CNN Model Compression. CVPR Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung
2022	J	`jnl`	CHEX: CHannel EXploration for CNN Model Compression. CoRR Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung
2022	A*	`conf`	Effective Model Sparsification by Scheduled Grow-and-Prune Methods. ICLR Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie
2022	J	`jnl`	Fairness via Adversarial Attribute Neighbourhood Robust Learning. CoRR Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang
2022	A*	`conf`	Improved Fine-Tuning by Better Leveraging Pre-Training Data. NeurIPS Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni B. Chan, Rong Jin
2022	A*	`conf`	Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency. CVPR Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang
2022	J	`jnl`	Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency. CoRR Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang
2021	J	`jnl`	A Novel Convergence Analysis for Algorithms of the Adam Family. CoRR Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2021	J	`jnl`	A Theoretical Analysis of Learning with Noisily Labeled Data. CoRR Yi Xu, Qi Qian, Hao Li, Rong Jin
2021	A*	`conf`	An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
2021	A*	`conf`	Dash: Semi-Supervised Learning with Dynamic Thresholding. ICML Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, Rong Jin
2021	J	`jnl`	Dash: Semi-Supervised Learning with Dynamic Thresholding. CoRR Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, Rong Jin
2021	J	`jnl`	Effective Model Sparsification by Scheduled Grow-and-Prune Methods. CoRR Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie
2021	A*	`conf`	Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
2021	J	`jnl`	Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
2021	J	`jnl`	Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice. CoRR Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Antoni B. Chan, Rong Jin
2021	J	`jnl`	On Stochastic Moving-Average Estimators for Non-Convex Optimization. CoRR Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
2021	J	`jnl`	On the Convergence of Deep Networks with Sample Quadratic Overparameterization. CoRR Asaf Noy, Yi Xu, Yonathan Aflalo, Rong Jin
2021	J	`jnl`	Self-Supervised Pre-Training for Transformer-Based Person Re-Identification. CoRR Hao Luo, Pichao Wang, Yi Xu, Feng Ding, Yanxin Zhou, Fan Wang, Hao Li, Rong Jin
2021	J	`jnl`	Why Does Multi-Epoch Training Help? CoRR Yi Xu, Qi Qian, Hao Li, Rong Jin
2020	J	`jnl`	A Practical Online Method for Distributionally Deep Robust Optimization. CoRR Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang
2020	J	`jnl`	Attentional Biased Stochastic Gradient for Imbalanced Classification. CoRR Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang
2020	A*	`conf`	Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
2020	J	`jnl`	Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
2020	A*	`conf`	Stochastic Optimization for Non-convex Inf-Projection Problems. ICML Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang
2020	J	`jnl`	Towards Understanding Label Smoothing. CoRR Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin
2020	J	`jnl`	WeMix: How to Better Utilize Data Augmentation. CoRR Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin
2019	A*	`conf`	Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. ICML Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang
2019	A	`conf`	Learning with Non-Convex Truncated Losses by SGD. UAI Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang
2019	A*	`conf`	Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS Yi Xu, Rong Jin, Tianbao Yang
2019	A*	`conf`	On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. IJCAI Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang
2019	A*	`conf`	Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang
2019	J	`jnl`	Stochastic Optimization for Non-convex Inf-Projection Problems. CoRR Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang
2019	J	`jnl`	Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang
2018	A*	`conf`	First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. NeurIPS Yi Xu, Rong Jin, Tianbao Yang
2018	J	`jnl`	Learning with Non-Convex Truncated Losses by SGD. CoRR Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang
2018	A*	`conf`	SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. ICML Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang
2017	—	`conf`	ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
2017	—	`conf`	Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS Yi Xu, Qihang Lin, Tianbao Yang
2017	A*	`conf`	Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang
2017	A*	`conf`	Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML Yi Xu, Qihang Lin, Tianbao Yang
2016	J	`jnl`	Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR Yi Xu, Qihang Lin, Tianbao Yang
2016	J	`jnl`	Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. CoRR Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang
2016	—	`conf`	Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang