Tianjin University, Tianjin, China, Ph.D., 06/2016
Dept. of Computer Science and Technology, Advisor: Prof. Shizhong Liao
Hebei University of Technology, Tianjin, China, B.S., 07/2009
Dept. of Information and Computing Science
Renmin University of China, Tenure-Track Associate Professor, 07/2021-Present
Renmin University of China, Tenure-Track Assistant Professor, 08/2020-07/2021
Institute of Information Engineering, CAS, Associate Researcher, 10/2018-08/2020
Institute of Information Engineering, CAS, Assistant Researcher, 06/2016-10/2018
-- Statistical Learning Theory
- Generalization Analysis in Complex Scenarios
[1]Introduction to Deep Learning, 51 class hours, Fall, 2023-2024
[2]Neural Networks and Deep Learning, 51 class hours, Fall, 2023-2024
[3]Mining of Massive Datasets, 51 class hours, Spring, 2022-2023
[4]Introduction to Deep Learning, 51 class hours, Fall, 2022-2023
[5]Neural Networks and Deep Learning, 51 class hours, Fall, 2022-2023
[6]Artificial Intelligence Integrated Design, 34 class hours, Summer 2021
[7]Mining of Massive Datasets, 51 class hours, Fall, 2021-2022
[8]Neural Networks and Deep Learning, 51 class hours, Fall, 2021-2022
[9]Neural Networks and Deep Learning, 51 class hours, Spring, 2020-2021
2025
Chemical knowledge-informed framework for privacy-aware retrosynthesis learning
Guikun Chen, Xu Zhang, Xiaolin Hu,Yong Liu,Yi Yang,Wenguan Wang
Nature Communications
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
Xiang Cheng, Chengyan Pan, Minjun Zhao, Deyang Li, Fangchao Liu, Xinyu Zhang, Xiao Zhang, Yong Liu
In ENNLP 2025
Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning
Changshuo Zhang, Ang Gao, Xiao Zhang, Yong Liu, Deyang Li, Fangchao Liu, Xinyu Zhang
In ENNLP 2025
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin
Hao Yi, Qingyang Li, Yulan Hu, Fuzheng Zhang, Di ZHANG, Yong Liu
In ENNLP 2025
Exploring the Limitations of Mamba in COPY and CoT Reasoning
Ruifeng Ren, Zhicong Li, Yong Liu
In ENNLP 2025
Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL
Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu
In ACL 2025
Towards Reward Fairness in RLHF: From a Resource Allocation Perspective
Sheng Ouyang, Yulan Hu, Ge Chen, Qingyang Li, Fuzheng Zhang, Yong Liu
In ACL 2025
The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models
Chen Qian, Dongrui Liu, Jie Zhang, Yong Liu, Jing Shao
In ACL 2025
Do not Abstain! Identify and Solve the Uncertainty
Jingyu Liu, JingquanPeng, xiaopeng Wu, Xubin Li, Tiezheng Ge, Bo Zheng, Yong Liu
In ACL 2025
Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization
Xinhao Yao ,Hongjin Qian,Xiaolin Hu,Gengze Xu,Wei Liu,Jian Luan,Bin Wang,Yong Liu
In IJCAI 2025
Towards Improved Risk Bounds for Transductive Learning
Bowei Zhu, Shaojie Li, Yong Liu
In IJCAI 2025
Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning
Zeyu Gan, Yun Liao, Yong Liu
In ICML 2025
Understanding Model Ensemble in Transferable Adversarial Attack
Wei Yao, Zeliang Zhang, Huayi Tang, Yong Liu
In ICML 2025
Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization
Zixuan Gong, Xiaolin Hu, Huayi Tang, Yong Liu
In ICLR 2025
Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective
Zeyu Gan, Yong Liu
In ICLR 2025
ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Pengwei Tang, Xiaolin Hu, Yong Liu
In ICLR 2025
Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization
Wenkai Yang, Shiqi Shen, Guangyao Shen, Wei Yao, Yong Liu, Gong Zhi, Yankai Lin, Ji-Rong Wen
In ICLR 2025
REEF: Representation Encoding Fingerprints for Large Language Models
Jie Zhang, Dongrui Liu, Chen Qian, Linfeng Zhang, Yong Liu, Yu Qiao, Jing Shao
In ICLR 2025
2024
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications
Huayi Tang, Yong Liu
JMLR
PATNAS: A Path-Based Training-Free Neural Architecture Search
Jiechao Yang, Yong Liu*, Wei Wang, Haoran Wu, Zhiyuan Chen, Xibo Ma*
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
Xinhao Yao , Xiaolin Hu , Shenzhi Yang , Yong Liu∗
In NeurIPS 2024
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens
Ruifeng Ren, Yong Liu*
In NeurIPS 2024
Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails
Shaojie Li, Yong Liu*
In JMLR
Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection
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
Pattern Recognition
Unbiased and augmentation-free self-supervised graph representation learning
Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang
Pattern Recognition
A survey on model compression for large language models
Xunyu Zhu, Jian Li, Yong Liu*, Can Ma, Weiping Wang
In TACL (CCF A)
Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem
Chen Qian, Huayi Tang, Hong Liang, Yong Liu*
In KDD
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models
Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu*, Jing Shao*
In ACL
ETAS: Zero-Shot Transformer Architecture Search via Network Trainability and Expressivity
Jiechao Yang, Yong Liu*
In ACL
Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses
Shaojie Li, Bowei Zhu, Yong Liu*
In ICML 2024
Concentration Inequalities for General Functions of Heavy-Tailed Random Variables
Shaojie Li, Yong Liu
In ICML 2024
Perfect Alignment May be Poisonous to Graph Contrastive Learning
Jingyu Liu, Huayi Tang, Yong Liu*
In ICML 2024
IdmMAE: Importance-Inspired Dynamic Masking for Graph Autoencoders
Ge Chen, Yulan Hu, Sheng Ouyang, Zhirui Yang, Yong Liu, Cuicui Luo
In SIGIR
Advancing Latent Representation Ranking for Masked Graph Autoencoder
Yulan Hu, Ge Chen, Sheng Ouyang, Zhirui Yang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Shangquan Wu, Zhao Cao, Yong Liu
In DASFAA 2024
Towards Sharper Risk Bounds for Minimax Problems
Bowei Zhu, Shaojie Li, Yong Liu
In IJCAI
On the Consistency and Large-Scale Extension of Multiple Kernel Clustering
Weixuan Liang, Chang Tang, Xinwang Liu, Yong Liu*, Jiyuan Liu, En Zhu, Kunlun He
IEEE TPAMI
High-dimensional analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm
Jian Li, Yong Liu*, Weiping Wang
In AAAI
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network
Ruyue Liu, Rong Yin, Yong Liu,Weiping Wang
In AAAI
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*
In AAAI
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li , Yong Liu*, Wei Wang , Haoran Wu, Weiping Wang
In AAAI
GFMAE: Self-Supervised GNN-Free Masked AutoEncoders
Yulan Hu, Sheng Ouyang, Zhirui Yang, Yi Zhao, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu
ICASSP
2023
Theoretical analysis of divide-and-conquer ERM: From the perspective of multi-view
Yun Liao, Yong Liu*, Shizhong Liao, Qinhua Hu, Jianwu Dang
Information Fusion
Optimal Rates for Agnostic Distributed Learning
Jian Li, Yong Liu*, Weiping Wang
IEEE Transactions on Information Theory
Optimal Convergence for Agnostic Kernel Learning With Random Feature
Jian Li, Yong Liu*, Weiping Wang
IEEE Transactions on Neural Networks and Learning Systems
In-context Learning with Transformer Is Really Equivalent to a Contrastive Learning Pattern
Ruifeng Ren, Yong Liu
Arxiv
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 Transactions on Neural Networks and Learning Systems
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 Transactions on Cybernetics
Improving Differentiable Architecture Search via Self-Distillation
Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
Neural Networks
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
Computers & Security
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping
Shaojie Li, Yong Liu*
In ECAI 2023
Optimal Convergence Rates for Distributed Nystrom Approximation
Jian Li, Yong Liu*, Weiping Wang
Journal of Machine Learning Research
Towards Understanding the Generalization of Graph Neural Networks
Huayi Tang and Yong Liu*
In ICML 2023
Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction
Shaojie Li, Yong Liu*
In ICML 2023
Optimal Convergence Rates for Agnostic Nystrom Kernel Learning
Jian Li, Yong Liu*, Weiping Wang
In ICML 2023
Consistency of Multiple Kernel Clustering
Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu
In ICML 2013
Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training
Penwei Tang, Wei Yao, Zhicong Li, Yong Liu*
In CVPR 2023
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search
Jiechao Yang, Yong Liu*
In CVPR 2023
Learning Rates for Nonconvex Pairwise Learning
Shaojie Li, Yong Liu*
IEEE Transactions on Pattern Analysis and Machine Intelligence (CCF A)
Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses
Xiaolin Hu, Shaojie Li, Yong Liu*
In ICLR
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms
Jian Li, Yong Liu*, Weiping Wang
Pattern Recognition
Understanding the Generalization Performance of Spectral Clustering Algorithms
Shaojie Li, Sheng Ouyang, Yong Liu*
In AAAI 2023 (CCF A)
2022
Scalable Kernel k-Means with Randomized Sketching: From Theory to Algorithm
Rong Yin, Yong Liu*, Xueyan Wang, Weiping Wang, Dan Meng
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022 (CCF A)
Non-IID Federated Learning with Sharper Risk Bound
Bojian Wei, Jian Li, Yong Liu and Weiping Wang
IEEE Transactions on Neural Networks and Learning Systems (SCI 一区)
Convolutional Spectral Kernel Learning with Generalization Guarantees
Jian Li, Yong Liu*, and Weiping Wang
Artificial Intelligence （CCF A）
Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms
Jiechao Guan, Yong Liu and Zhiwu Lu
In NeurIPS 2022 （CCF A）
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
In NeurIPS 2022 （CCF A）
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means
Rong Yin, Yong Liu, Weiping Wang and Dan Meng
In NeurIPS 2022 （CCF A）
Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth
康艺霖，刘勇*，李健，王伟平
In CIKM 2022 (CCF B)
基于稳定性分析的非凸在线点对学习的遗憾界
郎璇聪，李春生，刘勇，王梅
计算机研究与发展（第九界中国数据挖掘会议最佳论文）
Non-IID Distributed Learning with Optimal Mixture Weights
Jian Li, Bojian Wei, Yong Liu, Weiping Wang
In ECML 2022 (CCF B)
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails
Shaojie Li, Yong Liu*
In ICML 2022 (CCF A)
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
Huayi Tang and Yong Liu*
In ICML 2022 (CCF A)
Ridgeless Regression with Random Features
Jian Li , Yong Liu*, Yingying Zhang
In IJCAI 2022 (CCF A)
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase
Huayi Tang, Yong Liu*
CVPR 2022 (CCF A)
High Probability Generalization Bounds for Minimax Problems with Fast Rates
Shaojie Li, Yong Liu*
ICLR 2022
Distributed Randomized Sketching Kernel Learning
Rong Yin, Yong Liu*, Dang Men
AAAI (CCF A)
2021
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
Shaogao lv, Junhui Wang, Jiankun Liu, Yong Liu*
In: Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), CCF A, Spotlights (accept rate < 3%))
Towards Sharper Generalization Bounds for Structured Prediction
Shaojie Li, Yong Liu*
In: Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), CCF A
Refined Learning Bounds for Kernel and Approximate $k$-Means
Yong Liu
In: Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), CCF A, Spotlights (accept rate < 3%))
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
Wen Yu, Baiying Lei, Yong Liu, Zhiguang Feng, Yong Hu, Yanyan Shen, Shuqiang Wang, Michael K. Ng
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021 (SCI 一区) (To Appear)
Operation-level Progressive Differentiable Architecture Search
Xunyu Zhu, Jian Li, Yong Liu*, Weiping Wang
ICDM 2021
Federated Learning for Non-IID Data: From Theory to Algorithm (Best Student Paper)
Bojian Wei, Jian Li, Yong Liu*, Weiping Wang
Proceedings of the 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI)
General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning
Bowei Zhu, Yong Liu*
Proceedings of The 29th ACM International Conference on Multimedia (ACM MM) (CCF A)
Weighted distributed differential privacy ERM: Convex and non-convex
Yilin Kang, Yong Liu*, Ben Niu, Weiping Wang
Computers & Security (CCF B)
Distributed Nystrom Kernel Learning with Communications
Rong Yin, Yong Liu, Weiping Wang, Dan Meng
In: Proceedings of the 28th International Conference on Machine Learning (ICML), (CCF A)
Sharper Generalization Bounds for Clustering
Shaojie Li, Yong Liu*
In: Proceedings of the 28th International Conference on Machine Learning (ICML), (CCF A)
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
Yong Liu, Jiankun Liu, Shuqiang Wang
In: Proceedings of the 9th International Conference on Learning Representations (ICLR)
2020
Extremely sparse Johnson- Lindenstrauss transform: From Theory to Algorithm
Rong Yin, Yong Liu*, Weiping Wang, Dang Men
In: Proceedings of the 20th IEEE International Conference on Data Mining (ICDM), 2020:1376-1381 (CCF B)
Sketch Kernel Ridge Regression using Circulant Matrix: Algorithm and Theory.
Rong Yin, Yong Liu*, Weiping Wang, et al
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 31(9): 3512-3524, 2020 (SCI 一区)
Approximate Kernel Selection via Matrix Approximation.
Lizhong Ding, Shizhong Liao, Yong Liu, Li Liu, Fan Zhu, Yazhou Yao, Ling Shao, Xin Gao
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020. (CCF B)
Automated Spectral Kernel Learning.
Jian Li, Yong Liu*, Weiping Wang
In: Proceedings of 34th Conference on Artificial Intelligence (AAAI), 2020: 4618-4625. (CCF A)
Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm
Rong Yin, Yong Liu*, Lijing Lu, Weiping Wang, Dan Meng
Proceedings of 34th Conference on Artificial Intelligence (AAAI), 2020: 6696-6703. (CCF A)
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 (TPAMI), 2020,42(5):1083-1096. (CCF A)
2019
Kernel Stability for Model Selection in Kernel-based Algorithms.
Yong Liu, Shizhong Liao, Hua Zhang, et al
IEEE Transactions on Cybernetics (TCYB), 2019. Online, DOI: 10.1109/TCYB.2019. 2923824. （SCI一区）
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
In: Proceedings of 33th Conference on Artificial Intelligence (AAAI), 2019: 3462-3469.
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
In: Proceedings of 33th Conference on Artificial Intelligence (AAAI), 2019:3454-3461.
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 Transactions on Image Processing (TIP), 2019, 28(9):4486-4499. (CCF A)
Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis.
Jian Li, Yong Liu*, Rong Yin, et al
In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019:2887-2893. (CCF A)
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm.
Jian Li, Yong Liu*, Rong Yin , Weiping Wang
Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019: 2880-2886. (CCF A))
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
Advances in Neural Information Processing Systems 32 (NeurIPS), 2019:11257-11268. (CCF A)
2018
Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices.
Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao
Proceedings of 32rd Conference on Artificial Intelligence (AAAI), 2018: 2910-2917.
Fast Cross-Validation.
Yong Liu, Hailun Lin, Lizhong Ding, et al
In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2910-2917, 2018. (CCF A)
Multi-Class Learning: From Theory to Algorithm.
Jian Li, Yong Liu*, Rong Yin, et al
Advances in Neural Information Processing Systems 31 (NeurIPS), 1593-1602, 2018. (CCF A)
2017
Granularity selection for cross-validation of SVM.
Yong Liu, Shizhong Liao
Information Sciences, 2017, 475-483. ( CCF B)
Efficient Kernel Selection via Spectral Analysis.
Jian Li, Yong Liu, Hailun Lin
In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), 2017: 2124-2130. (CCF A)
Infinite Kernel Learning: Generalization Bounds and Algorithms.
Yong Liu, Shizhong Liao, Hailun Lin, et al
In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2017, 2280-2286. (CCF A)
Generalization Analysis for Ranking Using Integral Operator,
Yong Liu, Shizhong Liao, Linhai Lun, et al
In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2017: 2273-2279. (CCF A)
2016
基于积分算子空间显示描述的框架核选择方法
刘勇，廖士中
中国科学: 信息科学, 2016, 46(2), 165–178. (CCF A 中文期刊)
2015
Eigenvalues ratio for kernel selection of kernel methods
Yong Liu, Shizhong Liao
In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015: 2814–2820. (CCF A)
2014
Preventing Over-Fitting of Cross-Validation with Kernel Stability
Yong Liu, Shizhong Liao
In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), 2014:290–305. (CCF B)
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，2014, 51(10):2171-2177. (CCF A 中文期刊)
Kernel selection with spectral perturbation stability of kernel matrix
Yong Liu, Shizhong Liao
Science China Information Sciences, 2014, 57: 112103(10) (CCF B)
Efficient Approximation of Cross-validation for Kernel Methods using Bouligand Influence Function
Yong Liu, Shali Jiang, Shizhong Liao
In: Proceedings of The 31st International Conference on Machine Learning (ICML). 2014, 324-332. (CCF A)
2013
Eigenvalues Perturbation of Integral operator for Kernel Selection
Yong Liu, Shali Jiang, Shizhong Liao
In: Proceedings of the 22nd ACM International Conference on Information and Knowledge management (CIKM), 2013:2189-2198. (CCF B)
2011
Learning kernels with upper bounds of leave-one-out error
Yong Liu*, Shizhong Liao, Yuexuan-Hou
In: Proceedings of the 20th ACM International Conference on Information and Knowledge management (CIKM), 2011:2205-2208. (CCF B)
[1] Best Student Paper of PRICAI 2021
[2] “Outstanding Scholar” of Renmin University of China, 2020
[3] Selected as a member of Youth Innovation Promotion Association of Chinese Academy of Sciences, 2020
[4] Selected as a member of Excellent Young Technological Talents Program, Institute of Information Engineering, Chinese Academy of Sciences, 2017
[5] Best Paper Award of the 2nd PAKDD Doctoral Symposium on Data Mining
[6] National Scholarship, 2014
[7] Selected as National Academic Newcomer Award for Doctoral Students
[8] Excellent student scholarship, first prize for 5 times, 09/2011−07/2016
Guest Editor
Special Issue of “Statistical Machine Learning and Its Applications” of Mathematics
Journal Reviewer
Journal of Machine Learning Research (JMLR)
Artificial Intelligence (AI)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
IEEE Transactions on Cybernetics (TCYB), etc.
Conference Reviewer
International Conference on Machine Learning (ICML)
Neural Information Processing Systems (NeurIPS),
International Conference on Learning Representations (ICLR)
AAAI Conference on Artificial Intelligence (AAAI)
International Joint Conference on Artificial Intelligence (IJCAI), etc.
Social Service
Serve as the mentor of the Tencent Rhinoceros Scientific Talent Training plan for Middle School Students, 2021-2023
Selected as the Outstanding Mentor of Cultivation Top Talents of Tencent Rhinoceros Scientific Talent Training Plan
Email：iuyonggsai@gmail.com
Website：https://iie-liuyong.github.io/； https://dblp.uni-trier.de/pid/29/4867-18.html