Yong Liu

According to our database, Yong Liu authored at least 102 papers between 2011 and 2024.

Collaborative distances:

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

2024

Optimal Rates for Agnostic Distributed Learning.

Jian Li, Yong Liu, Weiping Wang

IEEE Trans. Inf. Theory, April, 2024

Theoretical analysis of divide-and-conquer ERM: From the perspective of multi-view.

Yun Liao, Yong Liu, Shizhong Liao, Qinghua Hu, Jianwu Dang

Inf. Fusion, March, 2024

WaveNet: Tackling Non-stationary Graph Signals via Graph Spectral Wavelets.

Zhirui Yang, Yulan Hu, Sheng Ouyang, Jingyu Liu, Shuqiang Wang, Xibo Ma, Wenhan Wang, Hanjing Su, Yong Liu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

ASWT-SGNN: Adaptive Spectral Wavelet Transform-Based Self-Supervised Graph Neural Network.

Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning.

Jian Li, Yong Liu, Weiping Wang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm.

Jian Li, Yong Liu, Weiping Wang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain.

Senrong You, Baiying Lei, Shuqiang Wang, Charles K. Chui, Albert C. Cheung, Yong Liu, Min Gan, Guo-Cheng Wu, Yanyan Shen

IEEE Trans. Neural Networks Learn. Syst., November, 2023

Improving Differentiable Architecture Search via self-distillation.

Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang

Neural Networks, October, 2023

Scalable Kernel $k$-Means With Randomized Sketching: From Theory to Algorithm.

Rong Yin, Yong Liu, Weiping Wang, Dan Meng

IEEE Trans. Knowl. Data Eng., September, 2023

Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN.

Wen Yu, Baiying Lei, Shuqiang Wang, Yong Liu, Zhiguang Feng, Yong Hu, Yanyan Shen, Michael K. Ng

IEEE Trans. Neural Networks Learn. Syst., August, 2023

Learning Rates for Nonconvex Pairwise Learning.

Shaojie Li, Yong Liu

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

Semi-supervised vector-valued learning: Improved bounds and algorithms.

Jian Li, Yong Liu, Weiping Wang

Pattern Recognit., June, 2023

Towards practical differential privacy in data analysis: Understanding the effect of epsilon on utility in private ERM.

Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu

Comput. Secur., May, 2023

Semantic-Aware Dehazing Network With Adaptive Feature Fusion.

Shengdong Zhang, Wenqi Ren, Xin Tan, Zhi-Jie Wang, Yong Liu, Jingang Zhang, Xiaoqin Zhang, Xiaochun Cao

IEEE Trans. Cybern., 2023

Optimal Convergence Rates for Distributed Nystroem Approximation.

Jian Li, Yong Liu, Weiping Wang

J. Mach. Learn. Res., 2023

ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network.

Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang

CoRR, 2023

Robust Neural Architecture Search.

Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang

CoRR, 2023

Operation-level Progressive Differentiable Architecture Search.

Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang

CoRR, 2023

Safe Contrastive Clustering.

Pengwei Tang, Huayi Tang, Wei Wang, Yong Liu

Proceedings of the MultiMedia Modeling - 29th International Conference, 2023

Towards Sharp Analysis for Distributed Learning with Random Features.

Jian Li, Yong Liu

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Towards Understanding Generalization of Graph Neural Networks.

Huayi Tang, Yong Liu

Proceedings of the International Conference on Machine Learning, 2023

Consistency of Multiple Kernel Clustering.

Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu

Proceedings of the International Conference on Machine Learning, 2023

Optimal Convergence Rates for Agnostic Nyström Kernel Learning.

Jian Li, Yong Liu, Weiping Wang

Proceedings of the International Conference on Machine Learning, 2023

Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction.

Shaojie Li, Yong Liu

Proceedings of the International Conference on Machine Learning, 2023

Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses.

Xiaolin Hu, Shaojie Li, Yong Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Data Heterogeneity Differential Privacy: From Theory to Algorithm.

Yilin Kang, Jian Li, Yong Liu, Weiping Wang

Proceedings of the Computational Science - ICCS 2023, 2023

HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search.

Jiechao Yang, Yong Liu, Hongteng Xu

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

Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training.

Pengwei Tang, Wei Yao, Zhicong Li, Yong Liu

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

Understanding the Generalization Performance of Spectral Clustering Algorithms.

Shaojie Li, Sheng Ouyang, Yong Liu

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

A Sketching Approach for Obtaining Real-Time Statistics Over Data Streams in Cloud.

Guangjun Wu, Xiaochun Yun, Yong Wang, Shupeng Wang, Binbin Li, Yong Liu

IEEE Trans. Cloud Comput., 2022

Sharper Utility Bounds for Differentially Private Models.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

CoRR, 2022

Stability and Generalization of Differentially Private Minimax Problems.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

CoRR, 2022

Convolutional spectral kernel learning with generalization guarantees.

Jian Li, Yong Liu, Weiping Wang

Artif. Intell., 2022

Non-IID Distributed Learning with Optimal Mixture Weights.

Jian Li, Bojian Wei, Yong Liu, Weiping Wang

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel.

Weixuan Liang, Xinwang Liu, Yong Liu, Sihang Zhou, Jun-Jie Huang, Siwei Wang, Jiyuan Liu, Yi Zhang, En Zhu

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

Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms.

Jiechao Guan, Yong Liu, Zhiwu Lu

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

Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means.

Rong Yin, Yong Liu, Weiping Wang, Dan Meng

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

Ridgeless Regression with Random Features.

Jian Li, Yong Liu, Yingying Zhang

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm.

Huayi Tang, Yong Liu

Proceedings of the International Conference on Machine Learning, 2022

High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails.

Shaojie Li, Yong Liu

Proceedings of the International Conference on Machine Learning, 2022

High Probability Generalization Bounds with Fast Rates for Minimax Problems.

Shaojie Li, Yong Liu

Proceedings of the Tenth International Conference on Learning Representations, 2022

Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase.

Huayi Tang, Yong Liu

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

Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Distributed Randomized Sketching Kernel Learning.

Rong Yin, Yong Liu, Dan Meng

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Kernel Stability for Model Selection in Kernel-Based Algorithms.

Yong Liu, Shizhong Liao, Hua Zhang, Wenqi Ren, Weiping Wang

IEEE Trans. Cybern., 2021

Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints.

Shaojie Li, Yong Liu

CoRR, 2021

Differential Privacy for Pairwise Learning: Non-convex Analysis.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

CoRR, 2021

Weighted distributed differential privacy ERM: Convex and non-convex.

Yilin Kang, Yong Liu, Ben Niu, Weiping Wang

Comput. Secur., 2021

Just Keep Your Concerns Private: Guaranteeing Heterogeneous Privacy and Achieving High Availability for ERM Algorithms.

Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu

Proceedings of the 20th IEEE International Conference on Trust, 2021

Federated Learning for Non-IID Data: From Theory to Algorithm.

Bojian Wei, Jian Li, Yong Liu, Weiping Wang

Proceedings of the PRICAI 2021: Trends in Artificial Intelligence, 2021

Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation.

Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu

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

Refined Learning Bounds for Kernel and Approximate $k$-Means.

Yong Liu

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

Towards Sharper Generalization Bounds for Structured Prediction.

Shaojie Li, Yong Liu

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

General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning.

Bowei Zhu, Yong Liu

Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

Energy-saving CNN with Clustering Channel Pruning.

Nannan Tian, Yong Liu, Weiping Wang, Dan Meng

Proceedings of the International Joint Conference on Neural Networks, 2021

Fast CNN Inference by Adaptive Sparse Matrix Decomposition.

Nannan Tian, Yong Liu, Weiping Wang, Dan Meng

Proceedings of the International Joint Conference on Neural Networks, 2021

Automatic CNN Compression Based on Hyper-parameter Learning.

Nannan Tian, Yong Liu, Weiping Wang, Dan Meng

Proceedings of the International Joint Conference on Neural Networks, 2021

Sharper Generalization Bounds for Clustering.

Shaojie Li, Yong Liu

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

Distributed Nyström Kernel Learning with Communications.

Rong Yin, Yong Liu, Weiping Wang, Dan Meng

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

Effective Distributed Learning with Random Features: Improved Bounds and Algorithms.

Yong Liu, Jiankun Liu, Shuqiang Wang

Proceedings of the 9th International Conference on Learning Representations, 2021

Operation-level Progressive Differentiable Architecture Search.

Xunyu Zhu, Jian Li, Yong Liu, Jun Liao, Weiping Wang

Proceedings of the IEEE International Conference on Data Mining, 2021

2020

Sketch Kernel Ridge Regression Using Circulant Matrix: Algorithm and Theory.

Rong Yin, Yong Liu, Weiping Wang, Dan Meng

IEEE Trans. Neural Networks Learn. Syst., 2020

Approximate Kernel Selection via Matrix Approximation.

Lizhong Ding, Shizhong Liao, Yong Liu, Li Liu, Fan Zhu, Yazhou Yao, Ling Shao, Xin Gao

IEEE Trans. Neural Networks Learn. Syst., 2020

Fast Cross-Validation for Kernel-Based Algorithms.

Yong Liu, Shizhong Liao, Shali Jiang, Lizhong Ding, Hailun Lin, Weiping Wang

IEEE Trans. Pattern Anal. Mach. Intell., 2020

Neural Architecture Optimization with Graph VAE.

Jian Li, Yong Liu, Jiankun Liu, Weiping Wang

CoRR, 2020

Nearly Optimal Clustering Risk Bounds for Kernel K-Means.

Yong Liu, Lizhong Ding, Weiping Wang

CoRR, 2020

Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS.

Yong Liu, Lizhong Ding, Weiping Wang

CoRR, 2020

Convolutional Spectral Kernel Learning.

Jian Li, Yong Liu, Weiping Wang

CoRR, 2020

Differentially Private ERM Based on Data Perturbation.

Yilin Kang, Yong Liu, Lizhong Ding, Xinwang Liu, Xinyi Tong, Weiping Wang

CoRR, 2020

Input Perturbation: A New Paradigm between Central and Local Differential Privacy.

Yilin Kang, Yong Liu, Ben Niu, Xinyi Tong, Likun Zhang, Weiping Wang

CoRR, 2020

Extremely Sparse Johnson-Lindenstrauss Transform: From Theory to Algorithm.

Rong Yin, Yong Liu, Weiping Wang, Dan Meng

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Hashing Based Prediction for Large-Scale Kernel Machine.

Lijing Lu, Rong Yin, Yong Liu, Weiping Wang

Proceedings of the Computational Science - ICCS 2020, 2020

Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm.

Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Automated Spectral Kernel Learning.

Jian Li, Yong Liu, Weiping Wang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval.

Hua Zhang, Peng She, Yong Liu, Jianhou Gan, Xiaochun Cao, Hassan Foroosh

IEEE Trans. Image Process., 2019

Weighted Distributed Differential Privacy ERM: Convex and Non-convex.

Yilin Kang, Yong Liu, Weiping Wang

CoRR, 2019

Learning Vector-valued Functions with Local Rademacher Complexity.

Jian Li, Yong Liu, Weiping Wang

CoRR, 2019

Distributed Learning with Random Features.

Jian Li, Yong Liu, Weiping Wang

CoRR, 2019

Efficient Cross-Validation for Semi-Supervised Learning.

Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang

CoRR, 2019

Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test.

Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao

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

Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis.

Jian Li, Yong Liu, Rong Yin, Weiping Wang

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

Multi-Class Learning using Unlabeled Samples: Theory and Algorithm.

Jian Li, Yong Liu, Rong Yin, Weiping Wang

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

Representation Learning of Taxonomies for Taxonomy Matching.

Hailun Lin, Yong Liu, Peng Zhang, Jianwu Wang

Proceedings of the Computational Science - ICCS 2019, 2019

Approximate Kernel Selection with Strong Approximate Consistency.

Lizhong Ding, Yong Liu, Shizhong Liao, Yu Li, Peng Yang, Yijie Pan, Chao Huang, Ling Shao, Xin Gao

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data.

Lizhong Ding, Zhi Liu, Yu Li, Shizhong Liao, Yong Liu, Peng Yang, Ge Yu, Ling Shao, Xin Gao

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Max-Diversity Distributed Learning: Theory and Algorithms.

Yong Liu, Jian Li, Weiping Wang

CoRR, 2018

Multi-Class Learning: From Theory to Algorithm.

Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang

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

Fast Cross-Validation.

Yong Liu, Hailun Lin, Lizhong Ding, Weiping Wang, Shizhong Liao

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices.

Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Granularity selection for cross-validation of SVM.

Yong Liu, Shizhong Liao

Inf. Sci., 2017

Efficient Kernel Selection via Spectral Analysis.

Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Learning Entity and Relation Embeddings for Knowledge Resolution.

Hailun Lin, Yong Liu, Weiping Wang, Yinliang Yue, Zheng Lin

Proceedings of the International Conference on Computational Science, 2017

Infinite Kernel Learning: Generalization Bounds and Algorithms.

Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang

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

Generalization Analysis for Ranking Using Integral Operator.

Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang

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

2015

Eigenvalues Ratio for Kernel Selection of Kernel Methods.

Yong Liu, Shizhong Liao

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Kernel selection with spectral perturbation stability of kernel matrix.

Yong Liu, Shizhong Liao

Sci. China Inf. Sci., 2014

Preventing Over-Fitting of Cross-Validation with Kernel Stability.

Yong Liu, Shizhong Liao

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function.

Yong Liu, Shali Jiang, Shizhong Liao

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Eigenvalues perturbation of integral operator for kernel selection.

Yong Liu, Shali Jiang, Shizhong Liao

Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013

2012

An Explicit Description of the Extended Gaussian Kernel.

Yong Liu, Shizhong Liao

Proceedings of the Emerging Trends in Knowledge Discovery and Data Mining - PAKDD 2012 International Workshops: DMHM, GeoDoc, 3Clust, and DSDM, Kuala Lumpur, Malaysia, May 29, 2012

2011

An Error Bound for Eigenvalues of Graph Laplacian with Bounded Kernel Function.

Yong Liu, Shizhong Liao

Proceedings of the Seventh International Conference on Computational Intelligence and Security, 2011

Learning kernels with upper bounds of leave-one-out error.

Yong Liu, Shizhong Liao, Yuexian Hou

Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011