Learning kernels with upper bounds of leave-one-out error. - Yong Liu 018; Shizhong Liao; Yuexian Hou (CIKM, 2011)

An Error Bound for Eigenvalues of Graph Laplacian with Bounded Kernel Function. - Yong Liu 018; Shizhong Liao (CIS, 2011)

An Explicit Description of the Extended Gaussian Kernel. - Yong Liu 018; Shizhong Liao (PAKDD Workshops, 2012)

Eigenvalues perturbation of integral operator for kernel selection. - Yong Liu 018; Shali Jiang 001; Shizhong Liao (CIKM, 2013)

Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function. - Yong Liu 018; Shali Jiang 001; Shizhong Liao (ICML, 2014)

Preventing Over-Fitting of Cross-Validation with Kernel Stability. - Yong Liu 018; Shizhong Liao (ECML/PKDD (2), 2014)

Kernel selection with spectral perturbation stability of kernel matrix. - Yong Liu 018; Shizhong Liao (Sci. China Inf. Sci., 2014)

Eigenvalues Ratio for Kernel Selection of Kernel Methods. - Yong Liu 018; Shizhong Liao (AAAI, 2015)

Generalization Analysis for Ranking Using Integral Operator. - Yong Liu 018; Shizhong Liao; Hailun Lin; Yinliang Yue; Weiping Wang 005 (AAAI, 2017)

Infinite Kernel Learning: Generalization Bounds and Algorithms. - Yong Liu 018; Shizhong Liao; Hailun Lin; Yinliang Yue; Weiping Wang 005 (AAAI, 2017)

Learning Entity and Relation Embeddings for Knowledge Resolution. - Hailun Lin; Yong Liu 018; Weiping Wang 005; Yinliang Yue; Zheng Lin 001 (ICCS, 2017)

Efficient Kernel Selection via Spectral Analysis. - Jian Li 040; Yong Liu 018; Hailun Lin; Yinliang Yue; Weiping Wang 005 (IJCAI, 2017)

Granularity selection for cross-validation of SVM. - Yong Liu 018; Shizhong Liao (Inf. Sci., 2017)

Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices. - Lizhong Ding 001; Shizhong Liao; Yong Liu 018; Peng Yang 010; Xin Gao 001 (AAAI, 2018)

Fast Cross-Validation. - Yong Liu 018; Hailun Lin; Lizhong Ding 001; Weiping Wang 005; Shizhong Liao (IJCAI, 2018)

Multi-Class Learning: From Theory to Algorithm. - Jian Li 040; Yong Liu 018; Rong Yin 001; Hua Zhang 008; Lizhong Ding 001; Weiping Wang 005 (NeurIPS, 2018)

Max-Diversity Distributed Learning: Theory and Algorithms. - Yong Liu 018; Jian Li 040; Weiping Wang 005 (CoRR, 2018)

Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data. - Lizhong Ding 001; Zhi Liu; Yu Li 006; Shizhong Liao; Yong Liu 018; Peng Yang 010; Ge Yu; Ling Shao 001; Xin Gao 001 (AAAI, 2019)

Approximate Kernel Selection with Strong Approximate Consistency. - Lizhong Ding 001; Yong Liu 018; Shizhong Liao; Yu Li 006; Peng Yang 010; Yijie Pan; Chao Huang; Ling Shao 001; Xin Gao 001 (AAAI, 2019)

Accelerating Real-Time Tracking Applications over Big Data Stream with Constrained Space. - Guangjun Wu; Xiaochun Yun; Shupeng Wang; Ge Fu; Chao Li; Yong Liu 018; Binbin Li 001; Yong Wang 032; Zhihui Zhao (DASFAA (1), 2019)

Representation Learning of Taxonomies for Taxonomy Matching. - Hailun Lin; Yong Liu 018; Peng Zhang; Jianwu Wang 001 (ICCS (1), 2019)

Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. - Jian Li 040; Yong Liu 018; Rong Yin 001; Weiping Wang 005 (IJCAI, 2019)

Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. - Jian Li 040; Yong Liu 018; Rong Yin 001; Weiping Wang 005 (IJCAI, 2019)

Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test. - Lizhong Ding 001; Mengyang Yu; Li Liu 004; Fan Zhu 001; Yong Liu 018; Yu Li 006; Ling Shao 001 (NeurIPS, 2019)

Efficient Cross-Validation for Semi-Supervised Learning. - Yong Liu 018; Jian Li 040; Guangjun Wu; Lizhong Ding 001; Weiping Wang 005 (CoRR, 2019)

Distributed Learning with Random Features. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (CoRR, 2019)

Learning Vector-valued Functions with Local Rademacher Complexity. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (CoRR, 2019)

Weighted Distributed Differential Privacy ERM: Convex and Non-convex. - Yilin Kang 002; Yong Liu 018; Weiping Wang 005 (CoRR, 2019)

Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval. - Hua Zhang 008; Peng She; Yong Liu 018; Jianhou Gan; Xiaochun Cao; Hassan Foroosh (IEEE Trans. Image Process., 2019)

Automated Spectral Kernel Learning. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (AAAI, 2020)

Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. - Rong Yin 001; Yong Liu 018; Lijing Lu; Weiping Wang 005; Dan Meng 002 (AAAI, 2020)

Hashing Based Prediction for Large-Scale Kernel Machine. - Lijing Lu; Rong Yin 001; Yong Liu 018; Weiping Wang 005 (ICCS (2), 2020)

Extremely Sparse Johnson-Lindenstrauss Transform: From Theory to Algorithm. - Rong Yin 001; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (ICDM, 2020)

Input Perturbation: A New Paradigm between Central and Local Differential Privacy. - Yilin Kang 002; Yong Liu 018; Ben Niu 001; Xinyi Tong 001; Likun Zhang; Weiping Wang 005 (CoRR, 2020)

Differentially Private ERM Based on Data Perturbation. - Yilin Kang 002; Yong Liu 018; Lizhong Ding 001; Xinwang Liu 002; Xinyi Tong 001; Weiping Wang 005 (CoRR, 2020)

Convolutional Spectral Kernel Learning. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (CoRR, 2020)

Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS. - Yong Liu 018; Lizhong Ding 001; Weiping Wang 005 (CoRR, 2020)

Nearly Optimal Clustering Risk Bounds for Kernel K-Means. - Yong Liu 018; Lizhong Ding 001; Weiping Wang 005 (CoRR, 2020)

Neural Architecture Optimization with Graph VAE. - Jian Li 040; Yong Liu 018; Jiankun Liu; Weiping Wang 005 (CoRR, 2020)

Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain. - Senrong You; Yong Liu 018; Baiying Lei; Shuqiang Wang (CoRR, 2020)

Fast Cross-Validation for Kernel-Based Algorithms. - Yong Liu 018; Shizhong Liao; Shali Jiang 001; Lizhong Ding 001; Hailun Lin; Weiping Wang 005 (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Approximate Kernel Selection via Matrix Approximation. - Lizhong Ding 001; Shizhong Liao; Yong Liu 018; Li Liu 004; Fan Zhu 001; Yazhou Yao; Ling Shao 001; Xin Gao 001 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Sketch Kernel Ridge Regression Using Circulant Matrix: Algorithm and Theory. - Rong Yin 001; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Operation-level Progressive Differentiable Architecture Search. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Jun Liao; Weiping Wang 005 (ICDM, 2021)

Effective Distributed Learning with Random Features: Improved Bounds and Algorithms. - Yong Liu 018; Jiankun Liu; Shuqiang Wang (ICLR, 2021)

Distributed Nyström Kernel Learning with Communications. - Rong Yin 001; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (ICML, 2021)

Sharper Generalization Bounds for Clustering. - Shaojie Li; Yong Liu 018 (ICML, 2021)

Automatic CNN Compression Based on Hyper-parameter Learning. - Nannan Tian; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (IJCNN, 2021)

Fast CNN Inference by Adaptive Sparse Matrix Decomposition. - Nannan Tian; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (IJCNN, 2021)

Energy-saving CNN with Clustering Channel Pruning. - Nannan Tian; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (IJCNN, 2021)

General Approximate Cross Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning. - Bowei Zhu; Yong Liu 018 (ACM Multimedia, 2021)

Towards Sharper Generalization Bounds for Structured Prediction. - Shaojie Li; Yong Liu 018 (NeurIPS, 2021)

Refined Learning Bounds for Kernel and Approximate $k$-Means. - Yong Liu 018 (NeurIPS, 2021)

Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation. - Shaogao Lv; Junhui Wang; Jiankun Liu; Yong Liu 018 (NeurIPS, 2021)

A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction. - Bowen Hu; Baiying Lei; Yanyan Shen; Yong Liu 018; Shuqiang Wang (PRCV (2), 2021)

Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis. - Junren Pan; Baiying Lei; Yanyan Shen; Yong Liu 018; Zhiguang Feng; Shuqiang Wang (PRCV (3), 2021)

Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction. - Qiankun Zuo; Baiying Lei; Yanyan Shen; Yong Liu 018; Zhiguang Feng; Shuqiang Wang (PRCV (3), 2021)

Federated Learning for Non-IID Data: From Theory to Algorithm. - Bojian Wei; Jian Li 040; Yong Liu 018; Weiping Wang 005 (PRICAI (1), 2021)

Just Keep Your Concerns Private: Guaranteeing Heterogeneous Privacy and Achieving High Availability for ERM Algorithms. - Yuzhe Li; Yong Liu 018; Bo Li 063; Weiping Wang 005; Nan Liu 011 (TrustCom, 2021)

Weighted distributed differential privacy ERM: Convex and non-convex. - Yilin Kang 002; Yong Liu 018; Ben Niu 001; Weiping Wang 005 (Comput. Secur., 2021)

Differential Privacy for Pairwise Learning: Non-convex Analysis. - Yilin Kang 002; Yong Liu 018; Jian Li 040; Weiping Wang 005 (CoRR, 2021)

Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints. - Shaojie Li; Yong Liu 018 (CoRR, 2021)

3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image. - Bowen Hu; Baiying Lei; Yong Liu 018; Min Gan; Bingchuan Wang; Shuqiang Wang (CoRR, 2021)

DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease. - Junren Pan; Baiying Lei; Shuqiang Wang; Bingchuan Wang; Yong Liu 018; Yanyan Shen (CoRR, 2021)

A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease. - Qiankun Zuo; Baiying Lei; Shuqiang Wang; Yong Liu 018; Bingchuan Wang; Yanyan Shen (CoRR, 2021)

Kernel Stability for Model Selection in Kernel-Based Algorithms. - Yong Liu 018; Shizhong Liao; Hua Zhang 008; Wenqi Ren; Weiping Wang 005 (IEEE Trans. Cybern., 2021)

Distributed Randomized Sketching Kernel Learning. - Rong Yin 001; Yong Liu 018; Dan Meng 002 (AAAI, 2022)

Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth. - Yilin Kang 002; Yong Liu 018; Jian Li 040; Weiping Wang 005 (CIKM, 2022)

Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase. - Huayi Tang; Yong Liu 018 (CVPR, 2022)

High Probability Generalization Bounds with Fast Rates for Minimax Problems. - Shaojie Li; Yong Liu 018 (ICLR, 2022)

High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails. - Shaojie Li; Yong Liu 018 (ICML, 2022)

Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm. - Huayi Tang; Yong Liu 018 (ICML, 2022)

Ridgeless Regression with Random Features. - Jian Li 040; Yong Liu 018; Yingying Zhang (IJCAI, 2022)

Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means. - Rong Yin 001; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (NeurIPS, 2022)

Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms. - Jiechao Guan; Yong Liu 018; Zhiwu Lu 001 (NeurIPS, 2022)

Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel. - Weixuan Liang; Xinwang Liu 002; Yong Liu 018; Sihang Zhou 001; Jun-Jie Huang 001; Siwei Wang 001; Jiyuan Liu 003; Yi Zhang 104; En Zhu (NeurIPS, 2022)

Non-IID Distributed Learning with Optimal Mixture Weights. - Jian Li 040; Bojian Wei; Yong Liu 018; Weiping Wang 005 (ECML/PKDD (4), 2022)

Convolutional spectral kernel learning with generalization guarantees. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (Artif. Intell., 2022)

Stability and Generalization of Differentially Private Minimax Problems. - Yilin Kang 002; Yong Liu 018; Jian Li 040; Weiping Wang 005 (CoRR, 2022)

Sharper Utility Bounds for Differentially Private Models. - Yilin Kang 002; Yong Liu 018; Jian Li 040; Weiping Wang 005 (CoRR, 2022)

A Sketching Approach for Obtaining Real-Time Statistics Over Data Streams in Cloud. - Guangjun Wu; Xiaochun Yun; Yong Wang 032; Shupeng Wang; Binbin Li 001; Yong Liu 018 (IEEE Trans. Cloud Comput., 2022)

Understanding the Generalization Performance of Spectral Clustering Algorithms. - Shaojie Li; Sheng Ouyang; Yong Liu 018 (AAAI, 2023)

Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training. - Pengwei Tang; Wei Yao 017; Zhicong Li; Yong Liu 018 (CVPR, 2023)

HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search. - Jiechao Yang; Yong Liu 018; Hongteng Xu (CVPR, 2023)

High Probability Analysis for Non-Convex Stochastic Optimization with Clipping. - Shaojie Li; Yong Liu 018 (ECAI, 2023)

Data Heterogeneity Differential Privacy: From Theory to Algorithm. - Yilin Kang 002; Jian Li 040; Yong Liu 018; Weiping Wang 005 (ICCS (1), 2023)

Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. - Xiaolin Hu; Shaojie Li; Yong Liu 018 (ICLR, 2023)

Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction. - Shaojie Li; Yong Liu 018 (ICML, 2023)

Optimal Convergence Rates for Agnostic Nyström Kernel Learning. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (ICML, 2023)

Consistency of Multiple Kernel Clustering. - Weixuan Liang; Xinwang Liu 002; Yong Liu 018; Chuan Ma 001; Yunping Zhao; Zhe Liu 001; En Zhu (ICML, 2023)

Towards Understanding Generalization of Graph Neural Networks. - Huayi Tang; Yong Liu 018 (ICML, 2023)

Towards Sharp Analysis for Distributed Learning with Random Features. - Jian Li 040; Yong Liu 018 (IJCAI, 2023)

Safe Contrastive Clustering. - Pengwei Tang; Huayi Tang; Wei Wang 315; Yong Liu 018 (MMM (1), 2023)

Operation-level Progressive Differentiable Architecture Search. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Weiping Wang 005 (CoRR, 2023)

Robust Neural Architecture Search. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Weiping Wang 005 (CoRR, 2023)

A Survey on Model Compression for Large Language Models. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Can Ma; Weiping Wang 005 (CoRR, 2023)

HGCVAE: Integrating Generative and Contrastive Learning for Heterogeneous Graph Learning. - Yulan Hu; Zhirui Yang; Sheng Ouyang; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang 006; Yong Liu 018 (CoRR, 2023)

Do We Really Need Contrastive Learning for Graph Representation? - Yulan Hu; Sheng Ouyang; Jingyu Liu; Ge Chen 006; Zhirui Yang; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang 006; Yong Liu 018 (CoRR, 2023)

LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts. - Sunhao Dai; Yuqi Zhou 001; Liang Pang; Weihao Liu; Xiaolin Hu; Yong Liu 018; Xiao Zhang 034; Jun Xu 001 (CoRR, 2023)

Optimal Convergence Rates for Distributed Nystroem Approximation. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (J. Mach. Learn. Res., 2023)

Semantic-Aware Dehazing Network With Adaptive Feature Fusion. - Shengdong Zhang; Wenqi Ren; Xin Tan 002; Zhi-Jie Wang 009; Yong Liu 018; Jingang Zhang; Xiaoqin Zhang 002; Xiaochun Cao (IEEE Trans. Cybern., 2023)

Towards practical differential privacy in data analysis: Understanding the effect of epsilon on utility in private ERM. - Yuzhe Li; Yong Liu 018; Bo Li 063; Weiping Wang 005; Nan Liu 011 (Comput. Secur., 2023)

Semi-supervised vector-valued learning: Improved bounds and algorithms. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (Pattern Recognit., 2023)

Learning Rates for Nonconvex Pairwise Learning. - Shaojie Li; Yong Liu 018 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN. - Wen Yu; Baiying Lei; Shuqiang Wang; Yong Liu 018; Zhiguang Feng; Yong Hu 003; Yanyan Shen; Michael K. Ng 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Scalable Kernel $k$-Means With Randomized Sketching: From Theory to Algorithm. - Rong Yin 001; Yong Liu 018; Weiping Wang 005; Dan Meng 002 (IEEE Trans. Knowl. Data Eng., 2023)

Improving Differentiable Architecture Search via self-distillation. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Weiping Wang 005 (Neural Networks, 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 018; Min Gan; Guo-Cheng Wu 001; Yanyan Shen (IEEE Trans. Neural Networks Learn. Syst., 2023)

High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (AAAI, 2024)

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (AAAI, 2024)

ASWT-SGNN: Adaptive Spectral Wavelet Transform-Based Self-Supervised Graph Neural Network. - Ruyue Liu; Rong Yin 001; Yong Liu 018; Weiping Wang 005 (AAAI, 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 018 (AAAI, 2024)

Advancing Latent Representation Ranking for Masked Graph Autoencoder. - Yulan Hu; Ge Chen 006; Sheng Ouyang; Zhirui Yang; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang 006; Zhao Cao; Shangquan Wu; Yong Liu 018 (DASFAA (6), 2024)

GFMAE: Self-Supervised GNN-Free Masked Autoencoders. - Yulan Hu; Sheng Ouyang; Zhirui Yang; Yi Zhao 006; Junchen Wan; Fuzheng Zhang; Zhongyuan Wang 006; Yong Liu 018 (ICASSP, 2024)

Concentration Inequalities for General Functions of Heavy-Tailed Random Variables. - Shaojie Li; Yong Liu 018 (ICML, 2024)

Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses. - Shaojie Li; Bowei Zhu; Yong Liu 018 (ICML, 2024)

Perfect Alignment May be Poisonous to Graph Contrastive Learning. - Jingyu Liu; Huayi Tang; Yong Liu 018 (ICML, 2024)

Towards Sharper Risk Bounds for Minimax Problems. - Bowei Zhu; Shaojie Li; Yong Liu 018 (IJCAI, 2024)

Neural Retrievers are Biased Towards LLM-Generated Content. - Sunhao Dai; Yuqi Zhou 001; Liang Pang; Weihao Liu; Xiaolin Hu; Yong Liu 018; Xiao Zhang 034; Gang Wang 056; Jun Xu 001 (KDD, 2024)

Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem. - Chen Qian 006; Huayi Tang; Hong Liang; Yong Liu 018 (KDD, 2024)

IdmGAE: Importance-Inspired Dynamic Masking for Graph Autoencoders. - Ge Chen 006; Yulan Hu; Sheng Ouyang; Zhirui Yang; Yong Liu 018; Cuicui Luo (SIGIR, 2024)

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. - Jian Li 040; Yong Liu 018; Wei Wang 315; Haoran Wu; Weiping Wang 005 (CoRR, 2024)

Improving Small Language Models' Mathematical Reasoning via Equation-of-Thought Distillation. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Can Ma; Weiping Wang 005 (CoRR, 2024)

Exploring Task Unification in Graph Representation Learning via Generative Approach. - Yulan Hu; Sheng Ouyang; Zhirui Yang; Ge Chen 006; Junchen Wan; Xiao Wang; Yong Liu 018 (CoRR, 2024)

Towards Comprehensive Preference Data Collection for Reward Modeling. - Yulan Hu; Qingyang Li 001; Sheng Ouyang; Ge Chen 006; Kaihui Chen; Lijun Mei; Xucheng Ye; Fuzheng Zhang; Yong Liu 018 (CoRR, 2024)

Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation. - Ge Chen 006; Yulan Hu; Sheng Ouyang; Yong Liu 018; Cuicui Luo (CoRR, 2024)

TSO: Self-Training with Scaled Preference Optimization. - Kaihui Chen; Hao Yi; Qingyang Li 001; Tianyu Qi; Yulan Hu; Fuzheng Zhang; Yong Liu 018 (CoRR, 2024)

GUNDAM: Aligning Large Language Models with Graph Understanding. - Sheng Ouyang; Yulan Hu; Ge Chen 006; Yong Liu 018 (CoRR, 2024)

Video-Text Dataset Construction from Multi-AI Feedback: Promoting Weak-to-Strong Preference Learning for Video Large Language Models. - Hao Yi; Qingyang Li 001; Yulan Hu; Fuzheng Zhang; Di Zhang; Yong Liu 018 (CoRR, 2024)

Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey. - Yunkai Dang; Kaichen Huang; Jiahao Huo; Yibo Yan; Sirui Huang; Dongrui Liu; Mengxi Gao; Jie Zhang 012; Chen Qian 003; Kun Wang 042; Yong Liu 018; Jing Shao; et al. (CoRR, 2024)

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition. - Ruyue Liu; Rong Yin 001; Xiangzhen Bo; Xiaoshuai Hao; Xingrui Zhou; Yong Liu 018; Can Ma; Weiping Wang 005 (CoRR, 2024)

Towards sharper excess risk bounds for differentially private pairwise learning. - Yilin Kang 002; Jian Li 040; Yong Liu 018; Weiping Wang 005 (Neurocomputing, 2024)

Distilling mathematical reasoning capabilities into Small Language Models. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Can Ma; Weiping Wang 005 (Neural Networks, 2024)

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 018; Peng Yang 011; Tianfu Wang 001; Chuan-Ming Liu; Jichen Du; Xiaohua Xiao; Shuqiang Wang (Pattern Recognit., 2024)

Unbiased and augmentation-free self-supervised graph representation learning. - Ruyue Liu; Rong Yin 001; Yong Liu 018; Weiping Wang 005 (Pattern Recognit., 2024)

Theoretical analysis of divide-and-conquer ERM: From the perspective of multi-view. - Yun Liao; Yong Liu 018; Shizhong Liao; Qinghua Hu; Jianwu Dang (Inf. Fusion, 2024)

Optimal Rates for Agnostic Distributed Learning. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (IEEE Trans. Inf. Theory, 2024)

Non-IID Federated Learning With Sharper Risk Bound. - Bojian Wei; Jian Li 040; Yong Liu 018; Weiping Wang 005 (IEEE Trans. Neural Networks Learn. Syst., 2024)

On the Consistency and Large-Scale Extension of Multiple Kernel Clustering. - Weixuan Liang; Chang Tang; Xinwang Liu 002; Yong Liu 018; Jiyuan Liu 003; En Zhu; Kunlun He (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

AdaO2B: Adaptive Online to Batch Conversion for Out-of-Distribution Generalization. - Xiao Zhang 034; Sunhao Dai; Jun Xu 001; Yong Liu 018; Zhenhua Dong (AAAI, 2025)

Do not Abstain! Identify and Solve the Uncertainty. - Jingyu Liu; JingquanPeng JingquanPeng; Xiaopeng Wu; Xubin Li; Tiezheng Ge; Bo Zheng; Yong Liu 018 (ACL (1), 2025)

Towards Reward Fairness in RLHF: From a Resource Allocation Perspective. - Sheng Ouyang; Yulan Hu; Ge Chen 006; Qingyang Li 001; Fuzheng Zhang; Yong Liu 018 (ACL (1), 2025)

The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models. - Chen Qian 003; Dongrui Liu; Jie Zhang 012; Yong Liu 018; Jing Shao (ACL (1), 2025)

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL. - Wei Yao 017; Wenkai Yang; Ziqiao Wang; Yankai Lin; Yong Liu 018 (ACL (Findings), 2025)

Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective. - Zeyu Gan; Yong Liu 018 (ICLR, 2025)

Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization. - Zixuan Gong; Xiaolin Hu; Huayi Tang; Yong Liu 018 (ICLR, 2025)

ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning. - Pengwei Tang; Xiaolin Hu; Yong Liu 018 (ICLR, 2025)

Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization. - Wenkai Yang; Shiqi Shen; Guangyao Shen; Wei Yao 017; Yong Liu 018; Gong Zhi; Yankai Lin; Ji-Rong Wen (ICLR, 2025)

REEF: Representation Encoding Fingerprints for Large Language Models. - Jie Zhang 074; Dongrui Liu; Chen Qian 006; Linfeng Zhang 001; Yong Liu 018; Yu Qiao 001; Jing Shao (ICLR, 2025)

Adversarial Masked Graph Autoencoders for Improved Graph Representation Learning. - Yulan Hu; Zhirui Yang; Sheng Ouyang; Yong Liu 018 (ICMR, 2025)

Multi-Modal Molecular Representation Learning via Structure Awareness. - Rong Yin 001; Ruyue Liu; Xiaoshuai Hao; Xingrui Zhou; Yong Liu 018; Can Ma; Weiping Wang 005 (IEEE Trans. Image Process., 2025)

Coarse-to-Fine Process Reward Modeling for Enhanced Mathematical Reasoning. - Yulan Hu; Sheng Ouyang; Yong Liu 018 (CoRR, 2025)

Optimal Convergence for Agnostic Kernel Learning With Random Features. - Jian Li 040; Yong Liu 018; Weiping Wang 005 (IEEE Trans. Neural Networks Learn. Syst., 2025)

Understanding the Capabilities and Limitations of Weak-to-Strong Generalization. - Wei Yao 017; Wenkai Yang; Ziqiao Wang; Yankai Lin; Yong Liu 018 (CoRR, 2025)

SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin. - Hao Yi; Qingyang Li 001; Yulan Hu; Fuzheng Zhang; Di Zhang; Yong Liu 018 (CoRR, 2025)

AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning. - Ruyue Liu; Rong Yin 001; Yong Liu 018; Xiaoshuai Hao; Haichao Shi; Can Ma; Weiping Wang 005 (CoRR, 2025)

Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders. - Qijiong Liu; Jieming Zhu; Lu Fan; Kun Wang 042; Hengchang Hu; Wei Guo; Yong Liu 018; Xiao-Ming Wu 003 (CoRR, 2025)

PATNAS: A Path-Based Training-Free Neural Architecture Search. - Jiechao Yang; Yong Liu 018; Wei Wang 315; Haoran Wu; Zhiyuan Chen; Xibo Ma (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

On the Emergence of Weak-to-Strong Generalization: A Bias-Variance Perspective. - Gengze Xu; Wei Yao 017; Ziqiao Wang; Yong Liu 018 (CoRR, 2025)

Do not Abstain! Identify and Solve the Uncertainty. - Jingyu Liu; Jingquan Peng; Xiaopeng Wu; Xubin Li; Tiezheng Ge; Bo Zheng; Yong Liu 018 (CoRR, 2025)

On Weak-to-Strong Generalization and f-Divergence. - Wei Yao 017; Gengze Xu; Huayi Tang; Wenkai Yang; Donglin Di; Ziqiao Wang; Yong Liu 018 (CoRR, 2025)