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

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)

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)

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)

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

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

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

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

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

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)

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

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

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

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

Towards Sharper Risk Bounds for Agnostic Multi-objective Learning. - Bojian Wei; Jian Li 040; Weiping Wang 005 (IJCNN, 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)

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

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

Improving Differentiable Architecture Search via self-distillation. - Xunyu Zhu; Jian Li 040; Yong Liu 018; Weiping Wang 005 (Neural Networks, 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)

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)

Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model. - Xunyu Zhu; Jian Li 040; 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)

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)

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)