Publications (Google Scholar Profile)
———2022———
Non-IID Distributed Learning with Optimal Mixture Weights
Jian Li, Xuanyu Zhu, Yong Liu*. In ECML, 2022
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails
Shaojie Li, Yong Liu*. In ICML, 2022
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
Huayi Tang, Yong Liu*. In ICML, 2022
Ridgeless Regression with Random Features
Jian Li , Yong Liu*, Yingying Zhang. In IJCAI, 2022
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase
Huayi Tang, Yong Liu*. In CVPR, 2022
High Probability Generalization Bounds for Minimax Problems with Fast Rates
Shaojie Li, Yong Liu*. In ICLR, 2022
Distributed Randomized Sketching Kernel Learning
Rong Yin, Yong Liu*, Dang Men. In AAAI, 2022
———2021———
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
Shaogao lv, Junhui Wang, Jiankun Liu, Yong Liu*. In NeurIPS, 2021
Towards Sharper Generalization Bounds for Structured Prediction
Shaojie Li, Yong Liu*. In NeurIPS, 2021
Refined Learning Bounds for Kernel and Approximate kmeans
Yong Liu*. In NeurIPS, 2021
Operation-level Progressive Differentiable Architecture Search
Xunyu Zhu, Jian Li, Yong Liu*, Weiping Wang. In ICDM, 2021
Federated Learning for Non-IID Data: From Theory to Algorithm (Best Student Paper)
Bojian Wei, Jian Li, Yong Liu*, Weiping Wang. In PRICAI, 2021
General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning
Bowei Zhu, Yong Liu*. In ACM MM, 2021
Weighted Distributed Differential Privacy ERM: Convex and Non-convex
Yilin Kang, Yong Liu*, Ben Niu, Weiping Wang. Computers & Security, 2021
Distributed Nystrom Kernel Learning with Communications
Rong Yin, Yong Liu, Weiping Wang, Dan Meng. In ICML, 2021
Sharper Generalization Bounds for Clustering
Shaojie Li, Yong Liu*. In ICML, 2021
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
Yong Liu* iankun Liu, Shuqiang Wang. In ICLR, 2021
———2020———
Extremely sparse Johnson- Lindenstrauss transform: From Theory to Algorithm
Rong Yin, Yong Liu*, Weiping Wang, Dang Men. In ICDM，2020
Sketch Kernel Ridge Regression using Circulant Matrix: Algorithm and Theory
Rong Yin, Yong Liu*, Weiping Wang. IEEE Transactions on Neural Networks and Learning Systems, 2020
Automated Spectral Kernel Learning
Jian Li, Yong Liu*, Weiping Wang. In AAAI, 2020
Divide-and-Conquer Learning with Nystrom: Optimal Rate and Algorithm
Rong Yin, Yong Liu*, Lijing Lu, Weiping Wang, Dan Meng. In AAAI, 2020
Fast Cross-Validation for Kernel-based Algorithms
Yong Liu*, Shizhong Liao, Shali Jiang, Lizhong Ding, Hailun Lin, Weiping Wang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
———2019———
Kernel Stability for Model Selection in Kernel-based Algorithms
Yong Liu*, Shizhong Liao, Hua Zhang. IEEE Transactions on Cybernetics, 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. In IEEE TIP, 2019
Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis
Jian Li, Yong Liu*, Rong Yin. In IJCAI, 2019
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm
Jian Li, Yong Liu*, Rong Yin , Weiping Wang. In IJCAI, 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. In NeurIPS, 2019
———2018———
Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices
Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao. In AAAI, 2018
Max-Diversity Distributed Learning: Theory and Algorithms
Yong Liu, Jian Li, Weiping Wang. arXiv, 2018.
Multi-Class Learning: From Theory to Algorithm
Jian Li, Yong Liu*, Rong Yin, Hua Zhang, Li-zhong Ding, Weiping Wang. Advances in Neural Information Processing Systems 31 (NIPS), 2018, accept.
———2017———
Fast Cross-Validation
Yong Liu, Hailun Lin, Lizhong Ding, Weiping Wang, Shizhong Liao. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2910-2917, 2018
Generalization Analysis for Ranking Using Integral Operator
Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2272-2279, 2017
Infinite Kernel Learning: Generalization Bounds and Algorithms
Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2280-2286, 2017
Efficient Kernel Selection via Spectral Analysis
Jian Li, Yong Liu*, Hailun Lin, Yinliang Yue, Weiping Wang. Procedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2124-2130, 2017.
———2015———
Eigenvalues Ratio for Kernel Selection of Kernel Methods
Yong Liu, Shizhong Liao. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2814-2820, 2015
———2014———
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand influence function
Yong Liu, Shali Jiang, Shizhong Liao. Proceedings of the 31st International Conference on Machine Learning (ICML), 324-332, 2014
Approximate Kernel Selection with Strong Approximate Consistency
Lizhong Ding, Yong Liu, Shizhong Liao, Peng Yang, Yu Li, Yijie Pan, Chao Huang, Ling Shao, Xin Gao. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019, accept.
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 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019, accept.
Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices
Li-Zhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2497-2503, 2018
Granularity Selection for Cross-Validation of SVM
Yong Liu, Shizhong Liao. Information Sciences, 378:475-483, 2017
Learning Entity and Relation Embeddings for Knowledge Resolution
Hailun Lin, Yong Liu*, Weiping Wang, Yinliang Yue, Zheng Lin. Procedia Computer Science, 108:345-354, 2017
Preventing Over-Fitting of Cross-Validation with Kernel Stability
Yong Liu, Shizhong Liao. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML), 290-305, 2014
Kernel Selection with Spectral Perturbation Stability of Kernel Matrix
Yong Liu, Shizhong Liao. Science China Information Sciences 57(11):1-10, 2014
Error Analysis for Vector-Valued Regularized Least-Squares Algorithm
Yong Liu, Shizhong Liao. Proceedings on the International Conference on Artificial Intelligence (ICAI), 2014
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 (CIKM), 2189-2198, 2013
An Explicit Description of the Extended Gaussian Kernel
Yong Liu, Shizhong Liao. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 88-99, 2012 (Best Paper)
Learning Kernels with Upper Bounds of Leave-One-Out error
Yong Liu, Shizhong Liao, Yuexian Hou. Proceedings of the 20th ACM international Conference on Information and Knowledge Management (CIKM), 2205-2208, 2011
An Error Bound for Eigenvalues of Graph Laplacian with Bounded Kernel Function
Yong Liu, Shizhong Liao. Proceedings of Seventh International Conference on Computational Intelligence and Security (CIS), 435-440, 2011
Kernel Construction via Generalized Eigenvector Decomposition
Yong Liu, Shizhong Liao. Foundations of Intelligent Systems, 191-200, 2011
基于近似高斯核显式描述的大规模SVM求解
刘勇, 江沙里, 廖士中. 计算机研究与发展, 51(10):2171-2177, 2014
基于积分算子空间显式描述的框架核选择方法
刘勇, 廖士中. 中国科学:信息科学, 46(2):165-178, 2016
基于支持向量机泛化误差界的多核学习方法
刘勇, 廖士中. 武汉大学学报(理学版), 58(2), 2012