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ORCIDYong Liu 0018
Person information
- affiliation: Renmin University of China, China
- affiliation (former): Chinese Academy of Sciences, Institute of Information Engineering, Beijing, China
- affiliation (PhD 2016): Tianjin University, School of Computer Science and Technology, Tianjin, China
Other persons with the same name
- Yong Liu — disambiguation page
- Yong Liu 0001
![[0000-0003-3156-7002]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-3156-7002")
— Outreach Corporation, Seattle, WA, USA (and 3 more) - Yong Liu 0002 — Chinese Academy of Sciences, Institute of Automation, Brainnetome Center, Beijing, China
- Yong Liu 0003 — Peking University, College of Environmental Science and Engineering, Beijing, China
- Yong Liu 0004 — Nanjing University of Science and Technology, School of Computer Science and Engineering, China
- Yong Liu 0005 — Southwest Jiaotong University, Key Laboratory of Information Coding and Transmission, Chengdu, China
- Yong Liu 0006 — University of Tennessee, Knoxville, TN, USA
- Yong Liu 0007 — Zhejiang University, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, Hangzhou, China (and 1 more)
- Yong Liu 0008 — Wenzhou Medical University, School of Ophthalmology and Optometry, China
- Yong Liu 0009 — Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore
- Yong Liu 0010 — Aalto University School of Business, Department of Information and Service Economy, Aalto, Finland (and 3 more)
- Yong Liu 0011 — Jilin University, School of Mechanical Science and Engineering, Changchun, China
- Yong Liu 0012 — The University of Aizu, Aizu-Wakamatsu, Japan (and 4 more)
- Yong Liu 0013 — New York University, Tandon School of Engineering, Department of Electrical and Computer Engineering, Brooklyn, NY, USA (and 1 more)
- Yong Liu 0014 — Texas A&M University, College Station, USA
- Yong Liu 0015 — Nottingham Trent University, UK
- Yong Liu 0016 — Tianjin University, School of Electrical and Information Engineering, Tianjin, China
- Yong Liu 0017 — National University of Defense Technology, School of Electronic Science, Changsha, China
- Yong Liu 0019 — University of Science and Technology of China, School of Mathematical Sciences, Hefei, China
- Yong Liu 0020 — Huawei Noah's Ark Lab, Singapore (and 1 more)
- Yong Liu 0021 — Beijing Polytechnic, School of Telecommunication Engineering, Beijing, China
- Yong Liu 0022 — Jiangnan University, School of Business, Wuxi, China
- Yong Liu 0023 — Shanghai Jiao Tong University, China (and 3 more)
- Yong Liu 0024 — Indiana University, Department of Computer Science, Bloomington, IN, USA
- Yong Liu 0025 — Northwestern Polytechnical University, College of Automation, Xi'an, China
- Yong Liu 0026 — A*STAR, Artificial Intelligence Initiative, Singapore (and 2 more)
- Yong Liu 0027 — Beijing University of Posts and Telecommunications, School of Information and Communication Engineering, Beijing Key Laboratory of Network System Architecture and Convergence, China
- Yong Liu 0028 — Chongqing Jiaotong University, School of Economics and Management, Chongqing, China
- Yong Liu 0029 — Heilongjiang University, China
- Yong Liu 0030 — University of Chemical Technology, College of Information Science and Technology, Beijing, China
- Yong Liu 0031 — Xi'an Jiaotong University, National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, China (and 1 more)
- Yong Liu 0032 — YouTu Lab, Tencent, Shanghai, China (and 1 more)
- Yong Liu 0033 — Tsinghua University, Tsinghua Shenzhen International Graduate School, China
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2020 – today
- 2025
[j29]Jiechao Yang, Yong Liu, Wei Wang, Haoran Wu, Zhiyuan Chen, Xibo Ma:
PATNAS: A Path-Based Training-Free Neural Architecture Search. IEEE Trans. Pattern Anal. Mach. Intell. 47(3): 1484-1500 (2025)- [j28]Jian Li, Yong Liu, Weiping Wang:
Optimal Convergence for Agnostic Kernel Learning With Random Features. IEEE Trans. Neural Networks Learn. Syst. 36(1): 1779-1789 (2025) - [c78]Xiao Zhang, Sunhao Dai, Jun Xu, Yong Liu, Zhenhua Dong:
AdaO2B: Adaptive Online to Batch Conversion for Out-of-Distribution Generalization. AAAI 2025: 22596-22604 - [i50]Yulan Hu, Sheng Ouyang, Yong Liu:
Coarse-to-Fine Process Reward Modeling for Enhanced Mathematical Reasoning. CoRR abs/2501.13622 (2025) - [i49]Ruyue Liu, Rong Yin, Yong Liu, Xiaoshuai Hao, Haichao Shi, Can Ma, Weiping Wang:
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning. CoRR abs/2502.13525 (2025) - [i48]Zixuan Gong, Xiaolin Hu, Huayi Tang, Yong Liu:
Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization. CoRR abs/2502.17024 (2025) - 2024
- [j27]Yilin Kang, Jian Li, Yong Liu, Weiping Wang:
Towards sharper excess risk bounds for differentially private pairwise learning. Neurocomputing 610: 128610 (2024) - [j26]Yun Liao, Yong Liu, Shizhong Liao, Qinghua Hu, Jianwu Dang:
Theoretical analysis of divide-and-conquer ERM: From the perspective of multi-view. Inf. Fusion 103: 102087 (2024) - [j25]Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang:
Distilling mathematical reasoning capabilities into Small Language Models. Neural Networks 179: 106594 (2024) - [j24]Weixuan Liang, Chang Tang, Xinwang Liu, Yong Liu, Jiyuan Liu, En Zhu, Kunlun He:
On the Consistency and Large-Scale Extension of Multiple Kernel Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 46(10): 6935-6947 (2024) - [j23]Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang:
Unbiased and augmentation-free self-supervised graph representation learning. Pattern Recognit. 149: 110274 (2024) - [j22]Baiying Lei, Yu Liang, Jiayi Xie, You Wu, Enmin Liang, Yong Liu, Peng Yang, Tianfu Wang, Chuan-Ming Liu, Jichen Du, Xiaohua Xiao, Shuqiang Wang:
Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection. Pattern Recognit. 153: 110423 (2024) - [j21]Jian Li, Yong Liu, Weiping Wang:
Optimal Rates for Agnostic Distributed Learning. IEEE Trans. Inf. Theory 70(4): 2759-2778 (2024) - [j20]Bojian Wei, Jian Li, Yong Liu, Weiping Wang:
Non-IID Federated Learning With Sharper Risk Bound. IEEE Trans. Neural Networks Learn. Syst. 35(5): 6906-6917 (2024) - [c77]Zhirui Yang, Yulan Hu, Sheng Ouyang, Jingyu Liu, Shuqiang Wang, Xibo Ma, Wenhan Wang, Hanjing Su, Yong Liu:
WaveNet: Tackling Non-stationary Graph Signals via Graph Spectral Wavelets. AAAI 2024: 9287-9295 - [c76]Jian Li, Yong Liu, Weiping Wang:
High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm. AAAI 2024: 13500-13508 - [c75]Jian Li, Yong Liu, Weiping Wang:
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. AAAI 2024: 13509-13517 - [c74]Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang:
ASWT-SGNN: Adaptive Spectral Wavelet Transform-Based Self-Supervised Graph Neural Network. AAAI 2024: 13990-13998 - [c73]Yulan Hu, Ge Chen, Sheng Ouyang, Zhirui Yang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Zhao Cao, Shangquan Wu, Yong Liu:
Advancing Latent Representation Ranking for Masked Graph Autoencoder. DASFAA (6) 2024: 385-394 - [c72]Yulan Hu, Sheng Ouyang, Zhirui Yang, Yi Zhao, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu:
GFMAE: Self-Supervised GNN-Free Masked Autoencoders. ICASSP 2024: 7500-7504 - [c71]Shaojie Li, Yong Liu:
Concentration Inequalities for General Functions of Heavy-Tailed Random Variables. ICML 2024 - [c70]Shaojie Li, Bowei Zhu, Yong Liu:
Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses. ICML 2024 - [c69]Jingyu Liu, Huayi Tang, Yong Liu:
Perfect Alignment May be Poisonous to Graph Contrastive Learning. ICML 2024 - [c68]Bowei Zhu, Shaojie Li, Yong Liu:
Towards Sharper Risk Bounds for Minimax Problems. IJCAI 2024: 5698-5706 - [c67]Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong Liu, Xiao Zhang, Gang Wang, Jun Xu:
Neural Retrievers are Biased Towards LLM-Generated Content. KDD 2024: 526-537 - [c66]Chen Qian, Huayi Tang, Hong Liang, Yong Liu:
Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem. KDD 2024: 2444-2454 - [c65]Ge Chen, Yulan Hu, Sheng Ouyang, Zhirui Yang, Yong Liu, Cuicui Luo:
IdmGAE: Importance-Inspired Dynamic Masking for Graph Autoencoders. SIGIR 2024: 2457-2461 - [i47]Jian Li, Yong Liu, Wei Wang, Haoran Wu, Weiping Wang:
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. CoRR abs/2401.02734 (2024) - [i46]Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang:
Improving Small Language Models' Mathematical Reasoning via Equation-of-Thought Distillation. CoRR abs/2401.11864 (2024) - [i45]Yulan Hu, Sheng Ouyang, Zhirui Yang, Ge Chen, Junchen Wan, Xiao Wang, Yong Liu:
Exploring Task Unification in Graph Representation Learning via Generative Approach. CoRR abs/2403.14340 (2024) - [i44]Yulan Hu, Qingyang Li, Sheng Ouyang, Ge Chen, Kaihui Chen, Lijun Mei, Xucheng Ye, Fuzheng Zhang, Yong Liu:
Towards Comprehensive Preference Data Collection for Reward Modeling. CoRR abs/2406.16486 (2024) - [i43]Ge Chen, Yulan Hu, Sheng Ouyang, Yong Liu, Cuicui Luo:
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation. CoRR abs/2406.17517 (2024) - [i42]Kaihui Chen, Hao Yi, Qingyang Li, Tianyu Qi, Yulan Hu, Fuzheng Zhang, Yong Liu:
TSO: Self-Training with Scaled Preference Optimization. CoRR abs/2409.02118 (2024) - [i41]Sheng Ouyang, Yulan Hu, Ge Chen, Yong Liu:
GUNDAM: Aligning Large Language Models with Graph Understanding. CoRR abs/2409.20053 (2024) - [i40]Bowei Zhu, Shaojie Li, Yong Liu:
Towards Sharper Risk Bounds for Minimax Problems. CoRR abs/2410.08497 (2024) - [i39]Ruyue Liu, Rong Yin, Xiangzhen Bo, Xiaoshuai Hao, Xingrui Zhou, Yong Liu, Can Ma, Weiping Wang:
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition. CoRR abs/2412.13442 (2024) - 2023
- [j19]Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu:
Towards practical differential privacy in data analysis: Understanding the effect of epsilon on utility in private ERM. Comput. Secur. 128: 103147 (2023) - [j18]Jian Li, Yong Liu, Weiping Wang:
Optimal Convergence Rates for Distributed Nystroem Approximation. J. Mach. Learn. Res. 24: 141:1-141:39 (2023) - [j17]Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang:
Improving Differentiable Architecture Search via self-distillation. Neural Networks 167: 656-667 (2023) - [j16]Shaojie Li, Yong Liu:
Learning Rates for Nonconvex Pairwise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 9996-10011 (2023) - [j15]Jian Li, Yong Liu, Weiping Wang:
Semi-supervised vector-valued learning: Improved bounds and algorithms. Pattern Recognit. 138: 109356 (2023) - [j14]Shengdong Zhang, Wenqi Ren, Xin Tan, Zhi-Jie Wang, Yong Liu, Jingang Zhang, Xiaoqin Zhang, Xiaochun Cao:
Semantic-Aware Dehazing Network With Adaptive Feature Fusion. IEEE Trans. Cybern. 53(1): 454-467 (2023) - [j13]Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Scalable Kernel $k$-Means With Randomized Sketching: From Theory to Algorithm. IEEE Trans. Knowl. Data Eng. 35(9): 9210-9224 (2023) - [j12]Wen Yu, Baiying Lei, Shuqiang Wang, Yong Liu, Zhiguang Feng, Yong Hu, Yanyan Shen, Michael K. Ng:
Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN. IEEE Trans. Neural Networks Learn. Syst. 34(8): 4401-4415 (2023) - [j11]Senrong You, Baiying Lei, Shuqiang Wang, Charles K. Chui, Albert C. Cheung, Yong Liu, Min Gan, Guo-Cheng Wu, Yanyan Shen:
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain. IEEE Trans. Neural Networks Learn. Syst. 34(11): 8802-8814 (2023) - [c64]Shaojie Li, Sheng Ouyang, Yong Liu:
Understanding the Generalization Performance of Spectral Clustering Algorithms. AAAI 2023: 8614-8621 - [c63]Jiechao Yang, Yong Liu, Hongteng Xu:
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search. CVPR 2023: 11990-12000 - [c62]Pengwei Tang, Wei Yao, Zhicong Li, Yong Liu:
Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training. CVPR 2023: 24406-24416 - [c61]Shaojie Li, Yong Liu:
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping. ECAI 2023: 1406-1413 - [c60]Yilin Kang, Jian Li, Yong Liu, Weiping Wang:
Data Heterogeneity Differential Privacy: From Theory to Algorithm. ICCS (1) 2023: 119-133 - [c59]Xiaolin Hu, Shaojie Li, Yong Liu:
Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. ICLR 2023 - [c58]Shaojie Li, Yong Liu:
Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction. ICML 2023: 19789-19810 - [c57]Jian Li, Yong Liu, Weiping Wang:
Optimal Convergence Rates for Agnostic Nyström Kernel Learning. ICML 2023: 19811-19836 - [c56]Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu:
Consistency of Multiple Kernel Clustering. ICML 2023: 20650-20676 - [c55]Huayi Tang, Yong Liu:
Towards Understanding Generalization of Graph Neural Networks. ICML 2023: 33674-33719 - [c54]Jian Li, Yong Liu:
Towards Sharp Analysis for Distributed Learning with Random Features. IJCAI 2023: 3920-3928 - [c53]Pengwei Tang, Huayi Tang, Wei Wang, Yong Liu:
Safe Contrastive Clustering. MMM (1) 2023: 294-305 - [i38]Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang:
Improving Differentiable Architecture Search via Self-Distillation. CoRR abs/2302.05629 (2023) - [i37]Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang:
Operation-level Progressive Differentiable Architecture Search. CoRR abs/2302.05632 (2023) - [i36]Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang:
Robust Neural Architecture Search. CoRR abs/2304.02845 (2023) - [i35]Huayi Tang, Yong Liu:
Towards Understanding the Generalization of Graph Neural Networks. CoRR abs/2305.08048 (2023) - [i34]Shaojie Li, Yong Liu:
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping. CoRR abs/2307.13680 (2023) - [i33]Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang:
A Survey on Model Compression for Large Language Models. CoRR abs/2308.07633 (2023) - [i32]Yulan Hu, Zhirui Yang, Sheng Ouyang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu:
HGCVAE: Integrating Generative and Contrastive Learning for Heterogeneous Graph Learning. CoRR abs/2310.11102 (2023) - [i31]Yulan Hu, Sheng Ouyang, Jingyu Liu, Ge Chen, Zhirui Yang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu:
Do We Really Need Contrastive Learning for Graph Representation? CoRR abs/2310.14525 (2023) - [i30]Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong Liu, Xiao Zhang, Jun Xu:
LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts. CoRR abs/2310.20501 (2023) - [i29]Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang:
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network. CoRR abs/2312.05736 (2023) - 2022
- [j10]Jian Li, Yong Liu, Weiping Wang:
Convolutional spectral kernel learning with generalization guarantees. Artif. Intell. 313: 103803 (2022) - [j9]Guangjun Wu, Xiaochun Yun, Yong Wang, Shupeng Wang, Binbin Li, Yong Liu:
A Sketching Approach for Obtaining Real-Time Statistics Over Data Streams in Cloud. IEEE Trans. Cloud Comput. 10(2): 1462-1475 (2022) - [c52]Rong Yin, Yong Liu, Dan Meng:
Distributed Randomized Sketching Kernel Learning. AAAI 2022: 8883-8891 - [c51]Yilin Kang, Yong Liu, Jian Li, Weiping Wang:
Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth. CIKM 2022: 951-961 - [c50]Huayi Tang, Yong Liu:
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase. CVPR 2022: 202-211 - [c49]Shaojie Li, Yong Liu:
High Probability Generalization Bounds with Fast Rates for Minimax Problems. ICLR 2022 - [c48]Shaojie Li, Yong Liu:
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails. ICML 2022: 12931-12963 - [c47]Huayi Tang, Yong Liu:
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm. ICML 2022: 21090-21110 - [c46]Jian Li, Yong Liu, Yingying Zhang:
Ridgeless Regression with Random Features. IJCAI 2022: 3208-3214 - [c45]Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means. NeurIPS 2022 - [c44]Jiechao Guan, Yong Liu, Zhiwu Lu:
Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms. NeurIPS 2022 - [c43]Weixuan Liang, Xinwang Liu, Yong Liu, Sihang Zhou, Jun-Jie Huang, Siwei Wang, Jiyuan Liu, Yi Zhang, En Zhu:
Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel. NeurIPS 2022 - [c42]Jian Li, Bojian Wei, Yong Liu, Weiping Wang:
Non-IID Distributed Learning with Optimal Mixture Weights. ECML/PKDD (4) 2022: 539-554 - [i28]Yilin Kang, Yong Liu, Jian Li, Weiping Wang:
Stability and Generalization of Differentially Private Minimax Problems. CoRR abs/2204.04858 (2022) - [i27]Yilin Kang, Yong Liu, Jian Li, Weiping Wang:
Sharper Utility Bounds for Differentially Private Models. CoRR abs/2204.10536 (2022) - [i26]Shaojie Li, Sheng Ouyang, Yong Liu:
Understanding the Generalization Performance of Spectral Clustering Algorithms. CoRR abs/2205.00281 (2022) - [i25]Jian Li, Yong Liu, Yingying Zhang:
Ridgeless Regression with Random Features. CoRR abs/2205.00477 (2022) - [i24]Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu:
Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM. CoRR abs/2206.03488 (2022) - 2021
- [j8]Yilin Kang, Yong Liu, Ben Niu, Weiping Wang:
Weighted distributed differential privacy ERM: Convex and non-convex. Comput. Secur. 106: 102275 (2021) - [j7]Yong Liu, Shizhong Liao, Hua Zhang, Wenqi Ren, Weiping Wang:
Kernel Stability for Model Selection in Kernel-Based Algorithms. IEEE Trans. Cybern. 51(12): 5647-5658 (2021) - [c41]Xunyu Zhu, Jian Li, Yong Liu, Jun Liao, Weiping Wang:
Operation-level Progressive Differentiable Architecture Search. ICDM 2021: 1559-1564 - [c40]Yong Liu, Jiankun Liu, Shuqiang Wang:
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms. ICLR 2021 - [c39]Shaojie Li, Yong Liu:
Sharper Generalization Bounds for Clustering. ICML 2021: 6392-6402 - [c38]Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Distributed Nyström Kernel Learning with Communications. ICML 2021: 12019-12028 - [c37]Nannan Tian, Yong Liu, Weiping Wang, Dan Meng:
Automatic CNN Compression Based on Hyper-parameter Learning. IJCNN 2021: 1-8 - [c36]Nannan Tian, Yong Liu, Weiping Wang, Dan Meng:
Fast CNN Inference by Adaptive Sparse Matrix Decomposition. IJCNN 2021: 1-8 - [c35]Nannan Tian, Yong Liu, Weiping Wang, Dan Meng:
Energy-saving CNN with Clustering Channel Pruning. IJCNN 2021: 1-8 - [c34]Bowei Zhu, Yong Liu:
General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning. ACM Multimedia 2021: 5281-5289 - [c33]Yong Liu:
Refined Learning Bounds for Kernel and Approximate $k$-Means. NeurIPS 2021: 6142-6154 - [c32]Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu:
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. NeurIPS 2021: 21467-21479 - [c31]Shaojie Li, Yong Liu:
Towards Sharper Generalization Bounds for Structured Prediction. NeurIPS 2021: 26844-26857 - [c30]Bowen Hu, Baiying Lei, Yanyan Shen, Yong Liu, Shuqiang Wang:
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction. PRCV (2) 2021: 263-274 - [c29]Junren Pan, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang:
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis. PRCV (3) 2021: 467-478 - [c28]Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang:
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction. PRCV (3) 2021: 479-490 - [c27]Bojian Wei, Jian Li, Yong Liu, Weiping Wang:
Federated Learning for Non-IID Data: From Theory to Algorithm. PRICAI (1) 2021: 33-48 - [c26]Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu:
Just Keep Your Concerns Private: Guaranteeing Heterogeneous Privacy and Achieving High Availability for ERM Algorithms. TrustCom 2021: 371-378 - [i23]Yilin Kang, Yong Liu, Jian Li, Weiping Wang:
Differential Privacy for Pairwise Learning: Non-convex Analysis. CoRR abs/2105.03033 (2021) - [i22]Shaojie Li, Yong Liu:
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints. CoRR abs/2107.08686 (2021) - [i21]Bowen Hu, Baiying Lei, Yanyan Shen, Yong Liu, Shuqiang Wang:
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction. CoRR abs/2107.09923 (2021) - [i20]Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang:
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction. CoRR abs/2107.09928 (2021) - [i19]Junren Pan, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang:
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis. CoRR abs/2107.09953 (2021) - [i18]Bowen Hu, Baiying Lei, Yong Liu, Min Gan, Bingchuan Wang, Shuqiang Wang:
3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image. CoRR abs/2107.11010 (2021) - [i17]Junren Pan, Baiying Lei, Shuqiang Wang, Bingchuan Wang, Yong Liu, Yanyan Shen:
DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease. CoRR abs/2110.05712 (2021) - [i16]Qiankun Zuo, Baiying Lei, Shuqiang Wang, Yong Liu, Bingchuan Wang, Yanyan Shen:
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease. CoRR abs/2110.09302 (2021) - [i15]Shaojie Li, Yong Liu:
Learning Rates for Nonconvex Pairwise Learning. CoRR abs/2111.05232 (2021) - [i14]Wen Yu, Baiying Lei, Yanyan Shen, Shuqiang Wang, Yong Liu, Zhiguang Feng, Yong Hu, Michael K. Ng:
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN. CoRR abs/2111.12886 (2021) - 2020
- [j6]Yong Liu, Shizhong Liao, Shali Jiang, Lizhong Ding, Hailun Lin, Weiping Wang:
Fast Cross-Validation for Kernel-Based Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 42(5): 1083-1096 (2020) - [j5]Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Sketch Kernel Ridge Regression Using Circulant Matrix: Algorithm and Theory. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3512-3524 (2020) - [j4]Lizhong Ding, Shizhong Liao, Yong Liu, Li Liu, Fan Zhu, Yazhou Yao, Ling Shao, Xin Gao:
Approximate Kernel Selection via Matrix Approximation. IEEE Trans. Neural Networks Learn. Syst. 31(11): 4881-4891 (2020) - [c25]Jian Li, Yong Liu, Weiping Wang:
Automated Spectral Kernel Learning. AAAI 2020: 4618-4625 - [c24]Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng:
Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. AAAI 2020: 6696-6703 - [c23]Lijing Lu, Rong Yin, Yong Liu, Weiping Wang:
Hashing Based Prediction for Large-Scale Kernel Machine. ICCS (2) 2020: 496-509 - [c22]Rong Yin, Yong Liu, Weiping Wang, Dan Meng:
Extremely Sparse Johnson-Lindenstrauss Transform: From Theory to Algorithm. ICDM 2020: 1376-1381 - [i13]Yilin Kang, Yong Liu, Ben Niu, Xinyi Tong, Likun Zhang, Weiping Wang:
Input Perturbation: A New Paradigm between Central and Local Differential Privacy. CoRR abs/2002.08570 (2020) - [i12]Yilin Kang, Yong Liu, Lizhong Ding, Xinwang Liu, Xinyi Tong, Weiping Wang:
Differentially Private ERM Based on Data Perturbation. CoRR abs/2002.08578 (2020) - [i11]Jian Li, Yong Liu, Weiping Wang:
Convolutional Spectral Kernel Learning. CoRR abs/2002.12744 (2020) - [i10]Yong Liu, Lizhong Ding, Weiping Wang:
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS. CoRR abs/2003.03882 (2020) - [i9]Yong Liu, Lizhong Ding, Weiping Wang:
Nearly Optimal Clustering Risk Bounds for Kernel K-Means. CoRR abs/2003.03888 (2020) - [i8]Jian Li, Yong Liu, Jiankun Liu, Weiping Wang:
Neural Architecture Optimization with Graph VAE. CoRR abs/2006.10310 (2020) - [i7]Senrong You, Yong Liu, Baiying Lei, Shuqiang Wang:
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain. CoRR abs/2011.04145 (2020)
2010 – 2019
- 2019
- [j3]Hua Zhang, Peng She, Yong Liu, Jianhou Gan, Xiaochun Cao, Hassan Foroosh:
Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval. IEEE Trans. Image Process. 28(9): 4486-4499 (2019) - [c21]Lizhong Ding, Zhi Liu, Yu Li, Shizhong Liao, Yong Liu, Peng Yang, Ge Yu, Ling Shao, Xin Gao:
Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data. AAAI 2019: 3454-3461 - [c20]Lizhong Ding, Yong Liu, Shizhong Liao, Yu Li, Peng Yang, Yijie Pan, Chao Huang, Ling Shao, Xin Gao:
Approximate Kernel Selection with Strong Approximate Consistency. AAAI 2019: 3462-3469 - [c19]Guangjun Wu, Xiaochun Yun, Shupeng Wang, Ge Fu, Chao Li, Yong Liu, Binbin Li, Yong Wang, Zhihui Zhao:
Accelerating Real-Time Tracking Applications over Big Data Stream with Constrained Space. DASFAA (1) 2019: 3-18 - [c18]Hailun Lin, Yong Liu, Peng Zhang, Jianwu Wang:
Representation Learning of Taxonomies for Taxonomy Matching. ICCS (1) 2019: 383-397 - [c17]Jian Li, Yong Liu, Rong Yin, Weiping Wang:
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. IJCAI 2019: 2880-2886 - [c16]Jian Li, Yong Liu, Rong Yin, Weiping Wang:
Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. IJCAI 2019: 2887-2893 - [c15]Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao:
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test. NeurIPS 2019: 11257-11268 - [i6]Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang:
Efficient Cross-Validation for Semi-Supervised Learning. CoRR abs/1902.04768 (2019) - [i5]Jian Li, Yong Liu, Weiping Wang:
Distributed Learning with Random Features. CoRR abs/1906.03155 (2019) - [i4]Jian Li, Yong Liu, Weiping Wang:
Learning Vector-valued Functions with Local Rademacher Complexity. CoRR abs/1909.04883 (2019) - [i3]Jian Li, Yong Liu, Weiping Wang:
Automated Spectral Kernel Learning. CoRR abs/1909.04894 (2019) - [i2]Yilin Kang, Yong Liu, Weiping Wang:
Weighted Distributed Differential Privacy ERM: Convex and Non-convex. CoRR abs/1910.10308 (2019) - 2018
- [c14]Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao:
Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices. AAAI 2018: 2910-2917 - [c13]Yong Liu, Hailun Lin, Lizhong Ding, Weiping Wang, Shizhong Liao:
Fast Cross-Validation. IJCAI 2018: 2497-2503 - [c12]Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang:
Multi-Class Learning: From Theory to Algorithm. NeurIPS 2018: 1593-1602 - [i1]Yong Liu, Jian Li, Weiping Wang:
Max-Diversity Distributed Learning: Theory and Algorithms. CoRR abs/1812.07738 (2018) - 2017
- [j2]Yong Liu, Shizhong Liao:
Granularity selection for cross-validation of SVM. Inf. Sci. 378: 475-483 (2017) - [c11]Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang:
Generalization Analysis for Ranking Using Integral Operator. AAAI 2017: 2273-2279 - [c10]Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang:
Infinite Kernel Learning: Generalization Bounds and Algorithms. AAAI 2017: 2280-2286 - [c9]Hailun Lin, Yong Liu, Weiping Wang, Yinliang Yue, Zheng Lin:
Learning Entity and Relation Embeddings for Knowledge Resolution. ICCS 2017: 345-354 - [c8]Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang:
Efficient Kernel Selection via Spectral Analysis. IJCAI 2017: 2124-2130 - 2015
- [c7]Yong Liu, Shizhong Liao:
Eigenvalues Ratio for Kernel Selection of Kernel Methods. AAAI 2015: 2814-2820 - 2014
- [j1]Yong Liu, Shizhong Liao:
Kernel selection with spectral perturbation stability of kernel matrix. Sci. China Inf. Sci. 57(11): 1-10 (2014) - [c6]Yong Liu, Shali Jiang, Shizhong Liao:
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function. ICML 2014: 324-332 - [c5]Yong Liu, Shizhong Liao:
Preventing Over-Fitting of Cross-Validation with Kernel Stability. ECML/PKDD (2) 2014: 290-305 - 2013
- [c4]Yong Liu, Shali Jiang, Shizhong Liao:
Eigenvalues perturbation of integral operator for kernel selection. CIKM 2013: 2189-2198 - 2012
- [c3]Yong Liu, Shizhong Liao:
An Explicit Description of the Extended Gaussian Kernel. PAKDD Workshops 2012: 88-99 - 2011
- [c2]Yong Liu, Shizhong Liao, Yuexian Hou:
Learning kernels with upper bounds of leave-one-out error. CIKM 2011: 2205-2208 - [c1]Yong Liu, Shizhong Liao:
An Error Bound for Eigenvalues of Graph Laplacian with Bounded Kernel Function. CIS 2011: 436-440
Coauthor Index
Bai Ying Lei
aka: Baiying Lei
aka: Baiying Lei
[j28] [i49] [j27] [j25] [j23] [j21] [j20] [c76] [c75] [c74] [i47] [i46] [i39] [j19] [j18] [j17] [j15] [j13] [c60] [c57] [i38] [i37] [i36] [i33] [i29] [j10] [c51] [c45] [c42] [i28] [i27] [i24] [j8] [j7] [c41] [c38] [c37] [c36] [c35] [c27] [c26] [i23] [j6] [j5] [c25] [c24] [c23] [c22] [i13] [i12] [i11] [i10] [i9] [i8] [c17] [c16] [i6] [i5] [i4] [i3] [i2] [c13] [c12] [i1] [c11] [c10] [c9] [c8]
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