Yong Liu

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 83

Top Topics

Generalization Bounds

Kernel Learning

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Publications

Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang
Unbiased and augmentation-free self-supervised graph representation learning. Pattern Recognit. 149 (2024)
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 (2024)
Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang
ASWT-SGNN: Adaptive Spectral Wavelet Transform-Based Self-Supervised Graph Neural Network. AAAI (2024)
Jian Li, Yong Liu, Weiping Wang
Optimal Rates for Agnostic Distributed Learning. IEEE Trans. Inf. Theory 70 (4) (2024)
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 (2024)
Ge Chen, Yulan Hu, Sheng Ouyang, Yong Liu, Cuicui Luo
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation. CoRR (2024)
Bojian Wei, Jian Li, Yong Liu, Weiping Wang
Non-IID Federated Learning With Sharper Risk Bound. IEEE Trans. Neural Networks Learn. Syst. 35 (5) (2024)
Jian Li, Yong Liu, Weiping Wang
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. AAAI (2024)
Jian Li, Yong Liu, Weiping Wang
High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm. AAAI (2024)
Ge Chen, Yulan Hu, Sheng Ouyang, Zhirui Yang, Yong Liu, Cuicui Luo
IdmGAE: Importance-Inspired Dynamic Masking for Graph Autoencoders. SIGIR (2024)
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)
Yilin Kang, Jian Li, Yong Liu, Weiping Wang
Data Heterogeneity Differential Privacy: From Theory to Algorithm. ICCS (1) (2023)
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) (2023)
Shaojie Li, Yong Liu
Learning Rates for Nonconvex Pairwise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45 (8) (2023)
Jian Li, Yong Liu, Weiping Wang
Optimal Convergence Rates for Distributed Nystroem Approximation. J. Mach. Learn. Res. 24 (2023)
Huayi Tang, Yong Liu
Towards Understanding the Generalization of Graph Neural Networks. CoRR (2023)
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) (2023)
Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
Improving Differentiable Architecture Search via self-distillation. Neural Networks 167 (2023)
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) (2023)
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 (2023)
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) (2023)
Rong Yin, Yong Liu, Dan Meng
Distributed Randomized Sketching Kernel Learning. AAAI (2022)
Jian Li, Bojian Wei, Yong Liu, Weiping Wang
Non-IID Distributed Learning with Optimal Mixture Weights. ECML/PKDD (4) (2022)
Jiechao Guan, Yong Liu, Zhiwu Lu
Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms. NeurIPS (2022)
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) (2022)
Yilin Kang, Yong Liu, Jian Li, Weiping Wang
Sharper Utility Bounds for Differentially Private Models. CoRR (2022)
Shaojie Li, Sheng Ouyang, Yong Liu
Understanding the Generalization Performance of Spectral Clustering Algorithms. CoRR (2022)
Jian Li, Yong Liu, Yingying Zhang
Ridgeless Regression with Random Features. CoRR (2022)
Shaojie Li, Yong Liu
High Probability Generalization Bounds with Fast Rates for Minimax Problems. ICLR (2022)
Yilin Kang, Yong Liu, Jian Li, Weiping Wang
Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth. CIKM (2022)
Huayi Tang, Yong Liu
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase. CVPR (2022)
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 (2022)
Shaojie Li, Yong Liu
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails. ICML (2022)
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)
Huayi Tang, Yong Liu
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm. ICML (2022)
Yilin Kang, Yong Liu, Jian Li, Weiping Wang
Stability and Generalization of Differentially Private Minimax Problems. CoRR (2022)
Jian Li, Yong Liu, Weiping Wang
Convolutional spectral kernel learning with generalization guarantees. Artif. Intell. 313 (2022)
Rong Yin, Yong Liu, Weiping Wang, Dan Meng
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means. NeurIPS (2022)
Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong Liu
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model. NeurIPS (2021)
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)
Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. NeurIPS (2021)
Yilin Kang, Yong Liu, Ben Niu, Weiping Wang
Weighted distributed differential privacy ERM: Convex and non-convex. Comput. Secur. 106 (2021)
Nannan Tian, Yong Liu, Weiping Wang, Dan Meng
Fast CNN Inference by Adaptive Sparse Matrix Decomposition. IJCNN (2021)
Nannan Tian, Yong Liu, Weiping Wang, Dan Meng
Energy-saving CNN with Clustering Channel Pruning. IJCNN (2021)
Shaojie Li, Yong Liu
Sharper Generalization Bounds for Clustering. ICML (2021)
Rong Yin, Yong Liu, Weiping Wang, Dan Meng
Distributed Nyström Kernel Learning with Communications. ICML (2021)
Bowei Zhu, Yong Liu
General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning. ACM Multimedia (2021)
Bojian Wei, Jian Li, Yong Liu, Weiping Wang
Federated Learning for Non-IID Data: From Theory to Algorithm. PRICAI (1) (2021)
Yong Liu
Refined Learning Bounds for Kernel and Approximate $k$-Means. NeurIPS (2021)
Yong Liu, Jiankun Liu, Shuqiang Wang
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms. ICLR (2021)
Yong Liu, Shizhong Liao, Hua Zhang, Wenqi Ren, Weiping Wang
Kernel Stability for Model Selection in Kernel-Based Algorithms. IEEE Trans. Cybern. 51 (12) (2021)
Shaojie Li, Yong Liu
Towards Sharper Generalization Bounds for Structured Prediction. NeurIPS (2021)
Nannan Tian, Yong Liu, Weiping Wang, Dan Meng
Automatic CNN Compression Based on Hyper-parameter Learning. IJCNN (2021)
Jian Li, Yong Liu, Weiping Wang
Convolutional Spectral Kernel Learning. CoRR (2020)
Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng
Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. AAAI (2020)
Jian Li, Yong Liu, Weiping Wang
Automated Spectral Kernel Learning. AAAI (2020)
Rong Yin, Yong Liu, Weiping Wang, Dan Meng
Extremely Sparse Johnson-Lindenstrauss Transform: From Theory to Algorithm. ICDM (2020)
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) (2020)
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) (2020)
Yilin Kang, Yong Liu, Ben Niu, Xinyi Tong, Likun Zhang, Weiping Wang
Input Perturbation: A New Paradigm between Central and Local Differential Privacy. CoRR (2020)
Yilin Kang, Yong Liu, Lizhong Ding, Xinwang Liu, Xinyi Tong, Weiping Wang
Differentially Private ERM Based on Data Perturbation. CoRR (2020)
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) (2020)
Jian Li, Yong Liu, Weiping Wang
Automated Spectral Kernel Learning. CoRR (2019)
Jian Li, Yong Liu, Rong Yin, Weiping Wang
Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. IJCAI (2019)
Jian Li, Yong Liu, Rong Yin, Weiping Wang
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. IJCAI (2019)
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)
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)
Hailun Lin, Yong Liu, Peng Zhang, Jianwu Wang
Representation Learning of Taxonomies for Taxonomy Matching. ICCS (1) (2019)
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)
Yilin Kang, Yong Liu, Weiping Wang
Weighted Distributed Differential Privacy ERM: Convex and Non-convex. CoRR (2019)
Jian Li, Yong Liu, Weiping Wang
Distributed Learning with Random Features. CoRR (2019)
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) (2019)
Jian Li, Yong Liu, Weiping Wang
Learning Vector-valued Functions with Local Rademacher Complexity. CoRR (2019)
Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang
Efficient Cross-Validation for Semi-Supervised Learning. CoRR (2019)
Yong Liu, Hailun Lin, Lizhong Ding, Weiping Wang, Shizhong Liao
Fast Cross-Validation. IJCAI (2018)
Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao
Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices. AAAI (2018)
Yong Liu, Jian Li, Weiping Wang
Max-Diversity Distributed Learning: Theory and Algorithms. CoRR (2018)
Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang
Multi-Class Learning: From Theory to Algorithm. NeurIPS (2018)
Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang
Infinite Kernel Learning: Generalization Bounds and Algorithms. AAAI (2017)
Hailun Lin, Yong Liu, Weiping Wang, Yinliang Yue, Zheng Lin
Learning Entity and Relation Embeddings for Knowledge Resolution. ICCS (2017)
Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang
Generalization Analysis for Ranking Using Integral Operator. AAAI (2017)
Yong Liu, Shizhong Liao
Granularity selection for cross-validation of SVM. Inf. Sci. 378 (2017)
Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang
Efficient Kernel Selection via Spectral Analysis. IJCAI (2017)
Yong Liu, Shizhong Liao
Eigenvalues Ratio for Kernel Selection of Kernel Methods. AAAI (2015)
Yong Liu, Shizhong Liao
Preventing Over-Fitting of Cross-Validation with Kernel Stability. ECML/PKDD (2) (2014)
Yong Liu, Shizhong Liao
Kernel selection with spectral perturbation stability of kernel matrix. Sci. China Inf. Sci. 57 (11) (2014)
Yong Liu, Shali Jiang, Shizhong Liao
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function. ICML (2014)
Yong Liu, Shali Jiang, Shizhong Liao
Eigenvalues perturbation of integral operator for kernel selection. CIKM (2013)
Yong Liu, Shizhong Liao
An Explicit Description of the Extended Gaussian Kernel. PAKDD Workshops (2012)
Yong Liu, Shizhong Liao
An Error Bound for Eigenvalues of Graph Laplacian with Bounded Kernel Function. CIS (2011)
Yong Liu, Shizhong Liao, Yuexian Hou
Learning kernels with upper bounds of leave-one-out error. CIKM (2011)