Jian Li - RankMe

46 papers A* 11A 1B 3Journal 28Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	FedNK-RF: Federated Kernel Learning With Heterogeneous Data and Optimal Rates. IEEE Trans. Neural Networks Learn. Syst. Xuning Zhang, Jian Li, Rong Yin, Weiping Wang
2025	B	`conf`	Improving Mathematical Reasoning Abilities of Small Language Models via Key-Point-Driven Distillation. IJCNN Xunyu Zhu, Jian Li, Rong Yin, Can Ma, Weiping Wang
2025	B	`conf`	Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation. IJCNN Xunyu Zhu, Jian Li, Rong Yin, Can Ma, Weiping Wang
2025	J	`jnl`	Optimal Convergence for Agnostic Kernel Learning With Random Features. IEEE Trans. Neural Networks Learn. Syst. Jian Li, Yong Liu, Weiping Wang
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`	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	A*	`conf`	High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm. AAAI Jian Li, Yong Liu, Weiping Wang
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`	Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model. CoRR Xunyu Zhu, Jian Li, Can Ma, Weiping Wang
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`	Optimal Rates for Agnostic Distributed Learning. IEEE Trans. Inf. Theory Jian Li, Yong Liu, Weiping Wang
2024	J	`jnl`	Towards sharper excess risk bounds for differentially private pairwise learning. Neurocomputing Yilin Kang, Jian Li, Yong Liu, Weiping Wang
2023	J	`jnl`	A Survey on Model Compression for Large Language Models. CoRR Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang
2023	—	`conf`	Data Heterogeneity Differential Privacy: From Theory to Algorithm. ICCS (1) Yilin Kang, Jian Li, Yong Liu, Weiping Wang
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`	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`	Robust Neural Architecture Search. CoRR Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang
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	B	`conf`	Towards Sharper Risk Bounds for Agnostic Multi-objective Learning. IJCNN Bojian Wei, Jian Li, Weiping Wang
2022	J	`jnl`	Convolutional spectral kernel learning with generalization guarantees. Artif. Intell. Jian Li, Yong Liu, Weiping Wang
2022	—	`conf`	Non-IID Distributed Learning with Optimal Mixture Weights. ECML/PKDD (4) Jian Li, Bojian Wei, Yong Liu, Weiping Wang
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
2021	J	`jnl`	Differential Privacy for Pairwise Learning: Non-convex Analysis. CoRR Yilin Kang, Yong Liu, Jian Li, Weiping Wang
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`	Operation-level Progressive Differentiable Architecture Search. ICDM Xunyu Zhu, Jian Li, Yong Liu, Jun Liao, Weiping Wang
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`	Neural Architecture Optimization with Graph VAE. CoRR Jian Li, Yong Liu, Jiankun Liu, Weiping Wang
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 Vector-valued Functions with Local Rademacher Complexity. CoRR Jian Li, Yong Liu, Weiping Wang
2019	A*	`conf`	Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. IJCAI Jian Li, Yong Liu, Rong Yin, Weiping Wang
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
2017	A*	`conf`	Efficient Kernel Selection via Spectral Analysis. IJCAI Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang