Yong Liu - RankMe

226 papers A* 65A 4B 5C 2Misc 3Journal 129Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond the Black Box: Theory and Mechanism of Large Language Models. CoRR Zeyu Gan, Ruifeng Ren, Wei Yao, Xiaolin Hu, Gengze Xu, Chen Qian, Huayi Tang, Zixuan Gong, Xinhao Yao, Pengwei Tang, Zhenxing Dou, Yong Liu
2026	J	`jnl`	Learn More with Less: Uncertainty Consistency Guided Query Selection for RLVR. CoRR Hao Yi, Yulan Hu, Xin Li, Sheng Ouyang, Lizhong Ding, Yong Liu
2026	A*	`conf`	Put the Space of LoRA Initialization to the Extreme to Preserve Pre-trained Knowledge. AAAI Pengwei Tang, Xiaolin Hu, Yong Liu, Lizhong Ding, Dongjie Zhang, Xing Wu, Debing Zhang
2026	J	`jnl`	ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging. CoRR Junyao Yang, Chen Qian, Dongrui Liu, Wen Shen, Yong Liu, Jing Shao
2026	J	`jnl`	Sparsity is Combinatorial Depth: Quantifying MoE Expressivity via Tropical Geometry. CoRR Ye Su, Huayi Tang, Zixuan Gong, Yong Liu
2026	J	`jnl`	vCause: Efficient and Verifiable Causality Analysis for Cloud-based Endpoint Auditing. CoRR Qiyang Song, Qihang Zhou, Xiaoqi Jia, Zhenyu Song, Wenbo Jiang, Heqing Huang, Yong Liu, Dan Meng
2025	A*	`conf`	ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning. ICLR Pengwei Tang, Xiaolin Hu, Yong Liu
2025	J	`jnl`	ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning. CoRR Pengwei Tang, Xiaolin Hu, Yong Liu
2025	J	`jnl`	AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning. CoRR Ruyue Liu, Rong Yin, Yong Liu, Xiaoshuai Hao, Haichao Shi, Can Ma, Weiping Wang
2025	J	`jnl`	AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning. IEEE Trans. Multim. Ruyue Liu, Rong Yin, Yong Liu, Xiaoshuai Hao, Haichao Shi, Can Ma, Weiping Wang
2025	A*	`conf`	AdaO2B: Adaptive Online to Batch Conversion for Out-of-Distribution Generalization. AAAI Xiao Zhang, Sunhao Dai, Jun Xu, Yong Liu, Zhenhua Dong
2025	B	`conf`	Adversarial Masked Graph Autoencoders for Improved Graph Representation Learning. ICMR Yulan Hu, Zhirui Yang, Sheng Ouyang, Yong Liu
2025	J	`jnl`	Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders. CoRR Qijiong Liu, Jieming Zhu, Lu Fan, Kun Wang, Hengchang Hu, Wei Guo, Yong Liu, Xiao-Ming Wu
2025	J	`jnl`	CoT-Space: A Theoretical Framework for Internal Slow-Thinking via Reinforcement Learning. CoRR Zeyu Gan, Hao Yi, Yong Liu
2025	J	`jnl`	Coarse-to-Fine Process Reward Modeling for Enhanced Mathematical Reasoning. CoRR Yulan Hu, Sheng Ouyang, Yong Liu
2025	Misc	`conf`	Contrastive Pre-Training and Post-Tuning for Heterogeneous Graph Learning. ICASSP Yulan Hu, Sheng Ouyang, Zhirui Yang, Yong Liu
2025	J	`jnl`	Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning. CoRR Chen Qian, Dongrui Liu, Haochen Wen, Zhen Bai, Yong Liu, Jing Shao
2025	—	`conf`	Do not Abstain! Identify and Solve the Uncertainty. ACL (1) Jingyu Liu, JingquanPeng JingquanPeng, Xiaopeng Wu, Xubin Li, Tiezheng Ge, Bo Zheng, Yong Liu
2025	J	`jnl`	Do not Abstain! Identify and Solve the Uncertainty. CoRR Jingyu Liu, Jingquan Peng, Xiaopeng Wu, Xubin Li, Tiezheng Ge, Bo Zheng, Yong Liu
2025	J	`jnl`	LoRA-Null: Low-Rank Adaptation via Null Space for Large Language Models. CoRR Pengwei Tang, Yong Liu, Dongjie Zhang, Xing Wu, Debing Zhang
2025	J	`jnl`	Multi-Modal Molecular Representation Learning via Structure Awareness. CoRR Rong Yin, Ruyue Liu, Xiaoshuai Hao, Xingrui Zhou, Yong Liu, Can Ma, Weiping Wang
2025	J	`jnl`	Multi-Modal Molecular Representation Learning via Structure Awareness. IEEE Trans. Image Process. Rong Yin, Ruyue Liu, Xiaoshuai Hao, Xingrui Zhou, Yong Liu, Can Ma, Weiping Wang
2025	J	`jnl`	On Weak-to-Strong Generalization and f-Divergence. CoRR Wei Yao, Gengze Xu, Huayi Tang, Wenkai Yang, Donglin Di, Ziqiao Wang, Yong Liu
2025	J	`jnl`	On the Emergence of Weak-to-Strong Generalization: A Bias-Variance Perspective. CoRR Gengze Xu, Wei Yao, Ziqiao Wang, Yong Liu
2025	J	`jnl`	Optimal Convergence for Agnostic Kernel Learning With Random Features. IEEE Trans. Neural Networks Learn. Syst. Jian Li, Yong Liu, Weiping Wang
2025	J	`jnl`	PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification. CoRR Huayi Tang, Yong Liu
2025	J	`jnl`	PATNAS: A Path-Based Training-Free Neural Architecture Search. IEEE Trans. Pattern Anal. Mach. Intell. Jiechao Yang, Yong Liu, Wei Wang, Haoran Wu, Zhiyuan Chen, Xibo Ma
2025	A*	`conf`	REEF: Representation Encoding Fingerprints for Large Language Models. ICLR Jie Zhang, Dongrui Liu, Chen Qian, Linfeng Zhang, Yong Liu, Yu Qiao, Jing Shao
2025	A*	`conf`	Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning. ICML Zeyu Gan, Yun Liao, Yong Liu
2025	J	`jnl`	Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning. CoRR Zeyu Gan, Yun Liao, Yong Liu
2025	J	`jnl`	Rethinking Reward Miscalibration of GRPO in Agentic RL. CoRR Jingyu Liu, Xiaopeng Wu, Jingquan Peng, Kehan Chen, Chuan Yu, Lizhong Ding, Yong Liu
2025	J	`jnl`	Revisiting Transformers through the Lens of Low Entropy and Dynamic Sparsity. CoRR Ruifeng Ren, Yong Liu
2025	—	`conf`	Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL. ACL (Findings) Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu
2025	J	`jnl`	Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL. CoRR Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu
2025	—	`conf`	SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin. EMNLP (Findings) Hao Yi, Qingyang Li, Yulan Hu, Fuzheng Zhang, Di Zhang, Yong Liu
2025	J	`jnl`	SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin. CoRR Hao Yi, Qingyang Li, Yulan Hu, Fuzheng Zhang, Di Zhang, Yong Liu
2025	J	`jnl`	SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs. CoRR Ruyue Liu, Rong Yin, Xiangzhen Bo, Xiaoshuai Hao, Yong Liu, Jinwen Zhong, Can Ma, Weiping Wang
2025	A*	`conf`	Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization. ICLR Wenkai Yang, Shiqi Shen, Guangyao Shen, Wei Yao, Yong Liu, Gong Zhi, Yankai Lin, Ji-Rong Wen
2025	J	`jnl`	The Debate on RLVR Reasoning Capability Boundary: Shrinkage, Expansion, or Both? A Two-Stage Dynamic View. CoRR Xinhao Yao, Lu Yu, Xiaolin Hu, Fengwei Teng, Qing Cui, Jun Zhou, Yong Liu
2025	—	`conf`	The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models. ACL (1) Chen Qian, Dongrui Liu, Jie Zhang, Yong Liu, Jing Shao
2025	J	`jnl`	Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization. CoRR Zixuan Gong, Xiaolin Hu, Huayi Tang, Yong Liu
2025	A*	`conf`	Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization. ICLR Zixuan Gong, Xiaolin Hu, Huayi Tang, Yong Liu
2025	A*	`conf`	Towards Improved Risk Bounds for Transductive Learning. IJCAI Bowei Zhu, Shaojie Li, Yong Liu
2025	—	`conf`	Towards Reward Fairness in RLHF: From a Resource Allocation Perspective. ACL (1) Sheng Ouyang, Yulan Hu, Ge Chen, Qingyang Li, Fuzheng Zhang, Yong Liu
2025	J	`jnl`	Towards Reward Fairness in RLHF: From a Resource Allocation Perspective. CoRR Sheng Ouyang, Yulan Hu, Ge Chen, Qingyang Li, Fuzheng Zhang, Yong Liu
2025	A*	`conf`	Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective. ICLR Zeyu Gan, Yong Liu
2025	J	`jnl`	Transformers as Intrinsic Optimizers: Forward Inference through the Energy Principle. CoRR Ruifeng Ren, Sheng Ouyang, Huayi Tang, Yong Liu
2025	A*	`conf`	Understanding Model Ensemble in Transferable Adversarial Attack. ICML Wei Yao, Zeliang Zhang, Huayi Tang, Yong Liu
2025	J	`jnl`	Understanding the Capabilities and Limitations of Weak-to-Strong Generalization. CoRR Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu
2025	J	`jnl`	Unveiling the Mechanisms of Explicit CoT Training: How Chain-of-Thought Enhances Reasoning Generalization. CoRR Xinhao Yao, Ruifeng Ren, Yun Liao, Yong Liu
2024	A*	`conf`	ASWT-SGNN: Adaptive Spectral Wavelet Transform-Based Self-Supervised Graph Neural Network. AAAI Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang
2024	—	`conf`	Advancing Latent Representation Ranking for Masked Graph Autoencoder. DASFAA (6) Yulan Hu, Ge Chen, Sheng Ouyang, Zhirui Yang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Zhao Cao, Shangquan Wu, Yong Liu
2024	A*	`conf`	Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses. ICML Shaojie Li, Bowei Zhu, Yong Liu
2024	J	`jnl`	Can Mamba Always Enjoy the "Free Lunch"? CoRR Ruifeng Ren, Zhicong Li, Yong Liu
2024	J	`jnl`	Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition. CoRR Ruyue Liu, Rong Yin, Xiangzhen Bo, Xiaoshuai Hao, Xingrui Zhou, Yong Liu, Can Ma, Weiping Wang
2024	A*	`conf`	Concentration Inequalities for General Functions of Heavy-Tailed Random Variables. ICML Shaojie Li, Yong Liu
2024	J	`jnl`	Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails. J. Mach. Learn. Res. Shaojie Li, Yong Liu
2024	J	`jnl`	DEAN: Deactivating the Coupled Neurons to Mitigate Fairness-Privacy Conflicts in Large Language Models. CoRR Chen Qian, Dongrui Liu, Jie Zhang, Yong Liu, Jing Shao
2024	J	`jnl`	DMQR-RAG: Diverse Multi-Query Rewriting for RAG. CoRR Zhicong Li, Jiahao Wang, Zhishu Jiang, Hangyu Mao, Zhongxia Chen, Jiazhen Du, Yuanxing Zhang, Fuzheng Zhang, Di Zhang, Yong Liu
2024	J	`jnl`	Distilling mathematical reasoning capabilities into Small Language Models. Neural Networks Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang
2024	A*	`conf`	Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective. NeurIPS Xinhao Yao, Xiaolin Hu, Shenzhi Yang, Yong Liu
2024	J	`jnl`	Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective. CoRR Xinhao Yao, Xiaolin Hu, Shenzhi Yang, Yong Liu
2024	J	`jnl`	Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey. CoRR Yunkai Dang, Kaichen Huang, Jiahao Huo, Yibo Yan, Sirui Huang, Dongrui Liu, Mengxi Gao, Jie Zhang, Chen Qian, Kun Wang, Yong Liu, Jing Shao, Hui Xiong, Xuming Hu
2024	J	`jnl`	Exploring Task Unification in Graph Representation Learning via Generative Approach. CoRR Yulan Hu, Sheng Ouyang, Zhirui Yang, Ge Chen, Junchen Wan, Xiao Wang, Yong Liu
2024	A*	`conf`	FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. AAAI Jian Li, Yong Liu, Weiping Wang
2024	J	`jnl`	FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. CoRR Jian Li, Yong Liu, Wei Wang, Haoran Wu, Weiping Wang
2024	Misc	`conf`	GFMAE: Self-Supervised GNN-Free Masked Autoencoders. ICASSP Yulan Hu, Sheng Ouyang, Zhirui Yang, Yi Zhao, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu
2024	J	`jnl`	GUNDAM: Aligning Large Language Models with Graph Understanding. CoRR Sheng Ouyang, Yulan Hu, Ge Chen, Yong Liu
2024	A*	`conf`	High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm. AAAI Jian Li, Yong Liu, Weiping Wang
2024	J	`jnl`	How Much Can RAG Help the Reasoning of LLM? CoRR Jingyu Liu, Jiaen Lin, Yong Liu
2024	J	`jnl`	Hybrid federated learning with brain-region attention network for multi-center Alzheimer's disease detection. Pattern Recognit. 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
2024	A*	`conf`	IdmGAE: Importance-Inspired Dynamic Masking for Graph Autoencoders. SIGIR Ge Chen, Yulan Hu, Sheng Ouyang, Zhirui Yang, Yong Liu, Cuicui Luo
2024	J	`jnl`	Improving Small Language Models' Mathematical Reasoning via Equation-of-Thought Distillation. CoRR Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang
2024	J	`jnl`	Information-Theoretic Generalization Bounds for Transductive Learning and its Applications. J. Mach. Learn. Res. Huayi Tang, Yong Liu
2024	A*	`conf`	Neural Retrievers are Biased Towards LLM-Generated Content. KDD Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong Liu, Xiao Zhang, Gang Wang, Jun Xu
2024	J	`jnl`	Non-IID Federated Learning With Sharper Risk Bound. IEEE Trans. Neural Networks Learn. Syst. Bojian Wei, Jian Li, Yong Liu, Weiping Wang
2024	J	`jnl`	On the Consistency and Large-Scale Extension of Multiple Kernel Clustering. IEEE Trans. Pattern Anal. Mach. Intell. Weixuan Liang, Chang Tang, Xinwang Liu, Yong Liu, Jiyuan Liu, En Zhu, Kunlun He
2024	J	`jnl`	Optimal Rates for Agnostic Distributed Learning. IEEE Trans. Inf. Theory Jian Li, Yong Liu, Weiping Wang
2024	A*	`conf`	Perfect Alignment May be Poisonous to Graph Contrastive Learning. ICML Jingyu Liu, Huayi Tang, Yong Liu
2024	J	`jnl`	Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation. CoRR Ge Chen, Yulan Hu, Sheng Ouyang, Yong Liu, Cuicui Luo
2024	J	`jnl`	REEF: Representation Encoding Fingerprints for Large Language Models. CoRR Jie Zhang, Dongrui Liu, Chen Qian, Linfeng Zhang, Yong Liu, Yu Qiao, Jing Shao
2024	A*	`conf`	Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem. KDD Chen Qian, Huayi Tang, Hong Liang, Yong Liu
2024	J	`jnl`	Stability and Sharper Risk Bounds with Convergence Rate O(1/n CoRR Bowei Zhu, Shaojie Li, Yong Liu
2024	J	`jnl`	TSO: Self-Training with Scaled Preference Optimization. CoRR Kaihui Chen, Hao Yi, Qingyang Li, Tianyu Qi, Yulan Hu, Fuzheng Zhang, Yong Liu
2024	J	`jnl`	Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization. CoRR Xinhao Yao, Hongjin Qian, Xiaolin Hu, Gengze Xu, Yong Liu
2024	J	`jnl`	Theoretical analysis of divide-and-conquer ERM: From the perspective of multi-view. Inf. Fusion Yun Liao, Yong Liu, Shizhong Liao, Qinghua Hu, Jianwu Dang
2024	J	`jnl`	Towards Comprehensive Preference Data Collection for Reward Modeling. CoRR Yulan Hu, Qingyang Li, Sheng Ouyang, Ge Chen, Kaihui Chen, Lijun Mei, Xucheng Ye, Fuzheng Zhang, Yong Liu
2024	A*	`conf`	Towards Sharper Risk Bounds for Minimax Problems. IJCAI Bowei Zhu, Shaojie Li, Yong Liu
2024	J	`jnl`	Towards Sharper Risk Bounds for Minimax Problems. CoRR Bowei Zhu, Shaojie Li, Yong Liu
2024	A*	`conf`	Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens. NeurIPS Ruifeng Ren, Yong Liu
2024	J	`jnl`	Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective. CoRR Zeyu Gan, Yong Liu
2024	J	`jnl`	Towards sharper excess risk bounds for differentially private pairwise learning. Neurocomputing Yilin Kang, Jian Li, Yong Liu, Weiping Wang
2024	J	`jnl`	Unbiased and augmentation-free self-supervised graph representation learning. Pattern Recognit. Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang
2024	J	`jnl`	Understanding Fairness Surrogate Functions in Algorithmic Fairness. Trans. Mach. Learn. Res. Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong Liu
2024	J	`jnl`	Understanding Model Ensemble in Transferable Adversarial Attack. CoRR Wei Yao, Zeliang Zhang, Huayi Tang, Yong Liu
2024	J	`jnl`	Video-Text Dataset Construction from Multi-AI Feedback: Promoting Weak-to-Strong Preference Learning for Video Large Language Models. CoRR Hao Yi, Qingyang Li, Yulan Hu, Fuzheng Zhang, Di Zhang, Yong Liu
2024	A*	`conf`	WaveNet: Tackling Non-stationary Graph Signals via Graph Spectral Wavelets. AAAI Zhirui Yang, Yulan Hu, Sheng Ouyang, Jingyu Liu, Shuqiang Wang, Xibo Ma, Wenhan Wang, Hanjing Su, Yong Liu
2023	J	`jnl`	A Survey on Model Compression for Large Language Models. CoRR Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang
2023	J	`jnl`	ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network. CoRR Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang
2023	J	`jnl`	Can Large Language Models Empower Molecular Property Prediction? CoRR Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang, Yong Liu
2023	A*	`conf`	Consistency of Multiple Kernel Clustering. ICML Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu
2023	—	`conf`	Data Heterogeneity Differential Privacy: From Theory to Algorithm. ICCS (1) Yilin Kang, Jian Li, Yong Liu, Weiping Wang
2023	C	`conf`	Decouple then Combine: A Simple and Effective Framework for Fraud Transaction Detection. ACML Pengwei Tang, Huayi Tang, Wenhan Wang, Hanjing Su, Yong Liu
2023	A*	`conf`	Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction. ICML Shaojie Li, Yong Liu
2023	J	`jnl`	Do We Really Need Contrastive Learning for Graph Representation? CoRR Yulan Hu, Sheng Ouyang, Jingyu Liu, Ge Chen, Zhirui Yang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu
2023	A*	`conf`	Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training. CVPR Pengwei Tang, Wei Yao, Zhicong Li, Yong Liu
2023	J	`jnl`	Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain. IEEE Trans. Neural Networks Learn. Syst. Senrong You, Baiying Lei, Shuqiang Wang, Charles K. Chui, Albert C. Cheung, Yong Liu, Min Gan, Guo-Cheng Wu, Yanyan Shen
2023	A*	`conf`	Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. ICLR Xiaolin Hu, Shaojie Li, Yong Liu
2023	J	`jnl`	HGCVAE: Integrating Generative and Contrastive Learning for Heterogeneous Graph Learning. CoRR Yulan Hu, Zhirui Yang, Sheng Ouyang, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Yong Liu
2023	A*	`conf`	HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search. CVPR Jiechao Yang, Yong Liu, Hongteng Xu
2023	A	`conf`	High Probability Analysis for Non-Convex Stochastic Optimization with Clipping. ECAI Shaojie Li, Yong Liu
2023	J	`jnl`	High Probability Analysis for Non-Convex Stochastic Optimization with Clipping. CoRR Shaojie Li, Yong Liu
2023	J	`jnl`	Improving Differentiable Architecture Search via Self-Distillation. CoRR Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
2023	J	`jnl`	Improving Differentiable Architecture Search via self-distillation. Neural Networks Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
2023	J	`jnl`	In-context Learning with Transformer Is Really Equivalent to a Contrastive Learning Pattern. CoRR Ruifeng Ren, Yong Liu
2023	J	`jnl`	Information-Theoretic Generalization Bounds for Transductive Learning and its Applications. CoRR Huayi Tang, Yong Liu
2023	J	`jnl`	LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts. CoRR Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong Liu, Xiao Zhang, Jun Xu
2023	J	`jnl`	Learning Rates for Nonconvex Pairwise Learning. IEEE Trans. Pattern Anal. Mach. Intell. Shaojie Li, Yong Liu
2023	J	`jnl`	Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN. IEEE Trans. Neural Networks Learn. Syst. Wen Yu, Baiying Lei, Shuqiang Wang, Yong Liu, Zhiguang Feng, Yong Hu, Yanyan Shen, Michael K. Ng
2023	J	`jnl`	Operation-level Progressive Differentiable Architecture Search. CoRR Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
2023	A*	`conf`	Optimal Convergence Rates for Agnostic Nyström Kernel Learning. ICML Jian Li, Yong Liu, Weiping Wang
2023	J	`jnl`	Optimal Convergence Rates for Distributed Nystroem Approximation. J. Mach. Learn. Res. Jian Li, Yong Liu, Weiping Wang
2023	J	`jnl`	Perfect Alignment May be Poisonous to Graph Contrastive Learning. CoRR Jingyu Liu, Huayi Tang, Yong Liu
2023	J	`jnl`	Robust Neural Architecture Search. CoRR Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
2023	—	`conf`	Safe Contrastive Clustering. MMM (1) Pengwei Tang, Huayi Tang, Wei Wang, Yong Liu
2023	J	`jnl`	Scalable Kernel $k$-Means With Randomized Sketching: From Theory to Algorithm. IEEE Trans. Knowl. Data Eng. Rong Yin, Yong Liu, Weiping Wang, Dan Meng
2023	J	`jnl`	Semantic-Aware Dehazing Network With Adaptive Feature Fusion. IEEE Trans. Cybern. Shengdong Zhang, Wenqi Ren, Xin Tan, Zhi-Jie Wang, Yong Liu, Jingang Zhang, Xiaoqin Zhang, Xiaochun Cao
2023	J	`jnl`	Semi-supervised vector-valued learning: Improved bounds and algorithms. Pattern Recognit. Jian Li, Yong Liu, Weiping Wang
2023	A*	`conf`	Towards Sharp Analysis for Distributed Learning with Random Features. IJCAI Jian Li, Yong Liu
2023	A*	`conf`	Towards Understanding Generalization of Graph Neural Networks. ICML Huayi Tang, Yong Liu
2023	J	`jnl`	Towards Understanding the Generalization of Graph Neural Networks. CoRR Huayi Tang, Yong Liu
2023	J	`jnl`	Towards practical differential privacy in data analysis: Understanding the effect of epsilon on utility in private ERM. Comput. Secur. Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu
2023	J	`jnl`	Understanding Fairness Surrogate Functions in Algorithmic Fairness. CoRR Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong Liu
2023	A*	`conf`	Understanding the Generalization Performance of Spectral Clustering Algorithms. AAAI Shaojie Li, Sheng Ouyang, Yong Liu
2023	J	`jnl`	VIGraph: Self-supervised Learning for Class-Imbalanced Node Classification. CoRR Yulan Hu, Sheng Ouyang, Zhirui Yang, Yong Liu
2022	J	`jnl`	A Sketching Approach for Obtaining Real-Time Statistics Over Data Streams in Cloud. IEEE Trans. Cloud Comput. Guangjun Wu, Xiaochun Yun, Yong Wang, Shupeng Wang, Binbin Li, Yong Liu
2022	J	`jnl`	Convolutional spectral kernel learning with generalization guarantees. Artif. Intell. Jian Li, Yong Liu, Weiping Wang
2022	A*	`conf`	Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm. ICML Huayi Tang, Yong Liu
2022	A*	`conf`	Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase. CVPR Huayi Tang, Yong Liu
2022	A*	`conf`	Distributed Randomized Sketching Kernel Learning. AAAI Rong Yin, Yong Liu, Dan Meng
2022	A*	`conf`	Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms. NeurIPS Jiechao Guan, Yong Liu, Zhiwu Lu
2022	A*	`conf`	High Probability Generalization Bounds with Fast Rates for Minimax Problems. ICLR Shaojie Li, Yong Liu
2022	A*	`conf`	High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails. ICML Shaojie Li, Yong Liu
2022	—	`conf`	Non-IID Distributed Learning with Optimal Mixture Weights. ECML/PKDD (4) Jian Li, Bojian Wei, Yong Liu, Weiping Wang
2022	A*	`conf`	Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means. NeurIPS Rong Yin, Yong Liu, Weiping Wang, Dan Meng
2022	A*	`conf`	Ridgeless Regression with Random Features. IJCAI Jian Li, Yong Liu, Yingying Zhang
2022	J	`jnl`	Ridgeless Regression with Random Features. CoRR Jian Li, Yong Liu, Yingying Zhang
2022	J	`jnl`	Sharper Utility Bounds for Differentially Private Models. CoRR Yilin Kang, Yong Liu, Jian Li, Weiping Wang
2022	A	`conf`	Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth. CIKM Yilin Kang, Yong Liu, Jian Li, Weiping Wang
2022	J	`jnl`	Stability and Generalization of Differentially Private Minimax Problems. CoRR Yilin Kang, Yong Liu, Jian Li, Weiping Wang
2022	A*	`conf`	Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel. NeurIPS Weixuan Liang, Xinwang Liu, Yong Liu, Sihang Zhou, Jun-Jie Huang, Siwei Wang, Jiyuan Liu, Yi Zhang, En Zhu
2022	J	`jnl`	Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM. CoRR Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu
2022	J	`jnl`	Understanding the Generalization Performance of Spectral Clustering Algorithms. CoRR Shaojie Li, Sheng Ouyang, Yong Liu
2021	J	`jnl`	3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image. CoRR Bowen Hu, Baiying Lei, Yong Liu, Min Gan, Bingchuan Wang, Shuqiang Wang
2021	—	`conf`	A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction. PRCV (2) Bowen Hu, Baiying Lei, Yanyan Shen, Yong Liu, Shuqiang Wang
2021	J	`jnl`	A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction. CoRR Bowen Hu, Baiying Lei, Yanyan Shen, Yong Liu, Shuqiang Wang
2021	J	`jnl`	A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease. CoRR Qiankun Zuo, Baiying Lei, Shuqiang Wang, Yong Liu, Bingchuan Wang, Yanyan Shen
2021	B	`conf`	Automatic CNN Compression Based on Hyper-parameter Learning. IJCNN Nannan Tian, Yong Liu, Weiping Wang, Dan Meng
2021	—	`conf`	Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis. PRCV (3) Junren Pan, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang
2021	J	`jnl`	Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis. CoRR Junren Pan, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang
2021	J	`jnl`	DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease. CoRR Junren Pan, Baiying Lei, Shuqiang Wang, Bingchuan Wang, Yong Liu, Yanyan Shen
2021	J	`jnl`	Differential Privacy for Pairwise Learning: Non-convex Analysis. CoRR Yilin Kang, Yong Liu, Jian Li, Weiping Wang
2021	A*	`conf`	Distributed Nyström Kernel Learning with Communications. ICML Rong Yin, Yong Liu, Weiping Wang, Dan Meng
2021	A*	`conf`	Effective Distributed Learning with Random Features: Improved Bounds and Algorithms. ICLR Yong Liu, Jiankun Liu, Shuqiang Wang
2021	B	`conf`	Energy-saving CNN with Clustering Channel Pruning. IJCNN Nannan Tian, Yong Liu, Weiping Wang, Dan Meng
2021	B	`conf`	Fast CNN Inference by Adaptive Sparse Matrix Decomposition. IJCNN Nannan Tian, Yong Liu, Weiping Wang, Dan Meng
2021	—	`conf`	Federated Learning for Non-IID Data: From Theory to Algorithm. PRICAI (1) Bojian Wei, Jian Li, Yong Liu, Weiping Wang
2021	A*	`conf`	General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning. ACM Multimedia Bowei Zhu, Yong Liu
2021	J	`jnl`	Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application. CoRR Yulan Hu, Yong Liu
2021	J	`jnl`	Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints. CoRR Shaojie Li, Yong Liu
2021	A*	`conf`	Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. NeurIPS Shaogao Lv, Junhui Wang, Jiankun Liu, Yong Liu
2021	B	`conf`	Just Keep Your Concerns Private: Guaranteeing Heterogeneous Privacy and Achieving High Availability for ERM Algorithms. TrustCom Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu
2021	J	`jnl`	Kernel Stability for Model Selection in Kernel-Based Algorithms. IEEE Trans. Cybern. Yong Liu, Shizhong Liao, Hua Zhang, Wenqi Ren, Weiping Wang
2021	J	`jnl`	Learning Rates for Nonconvex Pairwise Learning. CoRR Shaojie Li, Yong Liu
2021	J	`jnl`	Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN. CoRR Wen Yu, Baiying Lei, Yanyan Shen, Shuqiang Wang, Yong Liu, Zhiguang Feng, Yong Hu, Michael K. Ng
2021	—	`conf`	Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction. PRCV (3) Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang
2021	J	`jnl`	Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction. CoRR Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong Liu, Zhiguang Feng, Shuqiang Wang
2021	A*	`conf`	Operation-level Progressive Differentiable Architecture Search. ICDM Xunyu Zhu, Jian Li, Yong Liu, Jun Liao, Weiping Wang
2021	A*	`conf`	Refined Learning Bounds for Kernel and Approximate $k$-Means. NeurIPS Yong Liu
2021	A*	`conf`	Sharper Generalization Bounds for Clustering. ICML Shaojie Li, Yong Liu
2021	A*	`conf`	Towards Sharper Generalization Bounds for Structured Prediction. NeurIPS Shaojie Li, Yong Liu
2021	J	`jnl`	Weighted distributed differential privacy ERM: Convex and non-convex. Comput. Secur. Yilin Kang, Yong Liu, Ben Niu, Weiping Wang
2020	J	`jnl`	Approximate Kernel Selection via Matrix Approximation. IEEE Trans. Neural Networks Learn. Syst. Lizhong Ding, Shizhong Liao, Yong Liu, Li Liu, Fan Zhu, Yazhou Yao, Ling Shao, Xin Gao
2020	A*	`conf`	Automated Spectral Kernel Learning. AAAI Jian Li, Yong Liu, Weiping Wang
2020	J	`jnl`	Convolutional Spectral Kernel Learning. CoRR Jian Li, Yong Liu, Weiping Wang
2020	J	`jnl`	Differentially Private ERM Based on Data Perturbation. CoRR Yilin Kang, Yong Liu, Lizhong Ding, Xinwang Liu, Xinyi Tong, Weiping Wang
2020	A*	`conf`	Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. AAAI Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng
2020	A*	`conf`	Extremely Sparse Johnson-Lindenstrauss Transform: From Theory to Algorithm. ICDM Rong Yin, Yong Liu, Weiping Wang, Dan Meng
2020	J	`jnl`	Fast Cross-Validation for Kernel-Based Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. Yong Liu, Shizhong Liao, Shali Jiang, Lizhong Ding, Hailun Lin, Weiping Wang
2020	J	`jnl`	Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain. CoRR Senrong You, Yong Liu, Baiying Lei, Shuqiang Wang
2020	—	`conf`	Hashing Based Prediction for Large-Scale Kernel Machine. ICCS (2) Lijing Lu, Rong Yin, Yong Liu, Weiping Wang
2020	J	`jnl`	Input Perturbation: A New Paradigm between Central and Local Differential Privacy. CoRR Yilin Kang, Yong Liu, Ben Niu, Xinyi Tong, Likun Zhang, Weiping Wang
2020	J	`jnl`	Nearly Optimal Clustering Risk Bounds for Kernel K-Means. CoRR Yong Liu, Lizhong Ding, Weiping Wang
2020	J	`jnl`	Neural Architecture Optimization with Graph VAE. CoRR Jian Li, Yong Liu, Jiankun Liu, Weiping Wang
2020	J	`jnl`	Sketch Kernel Ridge Regression Using Circulant Matrix: Algorithm and Theory. IEEE Trans. Neural Networks Learn. Syst. Rong Yin, Yong Liu, Weiping Wang, Dan Meng
2020	J	`jnl`	Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS. CoRR Yong Liu, Lizhong Ding, Weiping Wang
2019	—	`conf`	Accelerating Real-Time Tracking Applications over Big Data Stream with Constrained Space. DASFAA (1) Guangjun Wu, Xiaochun Yun, Shupeng Wang, Ge Fu, Chao Li, Yong Liu, Binbin Li, Yong Wang, Zhihui Zhao
2019	A*	`conf`	Approximate Kernel Selection with Strong Approximate Consistency. AAAI Lizhong Ding, Yong Liu, Shizhong Liao, Yu Li, Peng Yang, Yijie Pan, Chao Huang, Ling Shao, Xin Gao
2019	A*	`conf`	Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. IJCAI Jian Li, Yong Liu, Rong Yin, Weiping Wang
2019	J	`jnl`	Automated Spectral Kernel Learning. CoRR Jian Li, Yong Liu, Weiping Wang
2019	J	`jnl`	Distributed Learning with Random Features. CoRR Jian Li, Yong Liu, Weiping Wang
2019	J	`jnl`	Efficient Cross-Validation for Semi-Supervised Learning. CoRR Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang
2019	J	`jnl`	Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval. IEEE Trans. Image Process. Hua Zhang, Peng She, Yong Liu, Jianhou Gan, Xiaochun Cao, Hassan Foroosh
2019	J	`jnl`	Learning Vector-valued Functions with Local Rademacher Complexity. CoRR Jian Li, Yong Liu, Weiping Wang
2019	A*	`conf`	Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data. AAAI Lizhong Ding, Zhi Liu, Yu Li, Shizhong Liao, Yong Liu, Peng Yang, Ge Yu, Ling Shao, Xin Gao
2019	A*	`conf`	Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. IJCAI Jian Li, Yong Liu, Rong Yin, Weiping Wang
2019	—	`conf`	Representation Learning of Taxonomies for Taxonomy Matching. ICCS (1) Hailun Lin, Yong Liu, Peng Zhang, Jianwu Wang
2019	A*	`conf`	Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test. NeurIPS Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao
2019	J	`jnl`	Weighted Distributed Differential Privacy ERM: Convex and Non-convex. CoRR Yilin Kang, Yong Liu, Weiping Wang
2018	A*	`conf`	Fast Cross-Validation. IJCAI Yong Liu, Hailun Lin, Lizhong Ding, Weiping Wang, Shizhong Liao
2018	J	`jnl`	Max-Diversity Distributed Learning: Theory and Algorithms. CoRR Yong Liu, Jian Li, Weiping Wang
2018	A*	`conf`	Multi-Class Learning: From Theory to Algorithm. NeurIPS Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang
2018	A*	`conf`	Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices. AAAI Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao
2017	A*	`conf`	Efficient Kernel Selection via Spectral Analysis. IJCAI Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang
2017	A*	`conf`	Generalization Analysis for Ranking Using Integral Operator. AAAI Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang
2017	J	`jnl`	Granularity selection for cross-validation of SVM. Inf. Sci. Yong Liu, Shizhong Liao
2017	A*	`conf`	Infinite Kernel Learning: Generalization Bounds and Algorithms. AAAI Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang
2017	Misc	`conf`	Learning Entity and Relation Embeddings for Knowledge Resolution. ICCS Hailun Lin, Yong Liu, Weiping Wang, Yinliang Yue, Zheng Lin
2015	A*	`conf`	Eigenvalues Ratio for Kernel Selection of Kernel Methods. AAAI Yong Liu, Shizhong Liao
2014	A*	`conf`	Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function. ICML Yong Liu, Shali Jiang, Shizhong Liao
2014	J	`jnl`	Kernel selection with spectral perturbation stability of kernel matrix. Sci. China Inf. Sci. Yong Liu, Shizhong Liao
2014	—	`conf`	Preventing Over-Fitting of Cross-Validation with Kernel Stability. ECML/PKDD (2) Yong Liu, Shizhong Liao
2013	A	`conf`	Eigenvalues perturbation of integral operator for kernel selection. CIKM Yong Liu, Shali Jiang, Shizhong Liao
2012	—	`conf`	An Explicit Description of the Extended Gaussian Kernel. PAKDD Workshops Yong Liu, Shizhong Liao
2011	C	`conf`	An Error Bound for Eigenvalues of Graph Laplacian with Bounded Kernel Function. CIS Yong Liu, Shizhong Liao
2011	A	`conf`	Learning kernels with upper bounds of leave-one-out error. CIKM Yong Liu, Shizhong Liao, Yuexian Hou