Publications (Google Scholar Profile)
* Corresponding author
Preprints
On the Statistical Optimality of Newton-type Federated Learning with Non-IID Data.
Jian Li, Yong Liu, Weiping Wang.
Submission in Journal of Machine Learning Research (JMLR), CCF-A Journal.Domain Agnostic Learning: Improved Algorithms and Bounds.
Jian Li, Yong Liu, Weiping Wang.
Submission in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), CCF-A Journal.A Survey on Model Compression for Large Language Models. [pdf]
Xunyu Zhu, Jian Li*, Yong Liu, Can Ma, Weiping Wang.
Submission in Transactions of the Association for Computational Linguistics (TACL). CCF-B Journal. arXiv:2308.07633.Small Language Models: Powerful Executors, Limited Thinkers.
Xunyu Zhu, Jian Li*, Yong Liu, Can Ma, Weiping Wang.
Submission in Transactions of the Association for Computational Linguistics (TACL). CCF-B Journal.
2024
High-dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm. [pdf] [poster] [code]
Jian Li, Yong Liu, Weiping Wang.
To appear in AAAI Conference on Artificial Intelligence (AAAI), 2024. CCF-A Conference.FedNS: A Fast Sketching Newton-type Algorithm for Federated Learning. [pdf] [poster] [code]
Jian Li, Yong Liu, Weiping Wang.
To appear in AAAI Conference on Artificial Intelligence (AAAI), 2024. CCF-A Conference.
2023
Optimal Rates for Agnostic Distributed Learning. [pdf] [code]
Jian Li, Yong Liu, Weiping Wang.
IEEE Transactions On Information Theory (TIT), 2023. CCF-A Journal.Optimal Convergence Rates for Distributed Nyström Approximation. [pdf] [code]
Jian Li, Yong Liu, Weiping Wang.
Journal of Machine Learning Research (JMLR), 2023. CCF-A Journal.Optimal Convergence Rates for Agnostic Nyström Kernel Learning. [pdf]
Jian Li, Yong Liu, Weiping Wang.
International Conference on Machine Learning (ICML), 2023. CCF-A Conference.Towards Sharp Analysis for Distributed Learning with Random Features. [pdf]
Jian Li, Yong Liu.
International Joint Conference on Artificial Intelligence (IJCAI), 2023. CCF-A Conference.Optimal Convergence for Agnostic Kernel Learning With Random Features. [pdf] [code]
Jian Li, Yong Liu, Weiping Wang.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023. CCF-B Journal.Semi-supervised vector-valued learning: Improved bounds and algorithms. [pdf]
Jian Li, Yong Liu, Weiping Wang.
Pattern Recognition (PR), 2023. CCF-B Journal.Improving Differentiable Architecture Search via Self-distillation. [pdf]
Xunyu Zhu, Jian Li*, Yong Liu, Weiping Wang.
Neural Networks, 2023. CCF-B Journal.Towards Sharper Risk Bounds for Agnostic Multi-Objectives Learning. [pdf]
Bojian Wei, Jian Li*, Yong Liu, Weiping Wang.
International Joint Conference on Neural Networks (IJCNN), 2023. CCF-C conference.Data Heterogeneity Differential Privacy: From Theory to Algorithm. [pdf]
Yiling Kang, Jian Li*, Yong Liu, Weiping Wang.
International Conference on Computational Science (ICCS), 2023.Robust Neural Architecture Search. [pdf]
Xunyu Zhu, Jian Li*, Yong Liu, Weiping Wang.
arXiv:2304.02845.
2022
Convolutional Spectral Kernel Learning with Generalization Guarantees. [pdf] [code]
Jian Li, Yong Liu, Weiping Wang.
Artificial Intelligence (AI), 2022. CCF-A Journal.Ridgeless Regression with Random Features. [pdf] [code]
Jian Li, Yong Liu, Yingying Zhang.
International Joint Conference on Artificial Intelligence (IJCAI), 2022. CCF-A Conference.Non-IID Distributed Learning with Optimal Mixture Weights. [pdf]
Jian Li, Bojian Wei, Yong Liu, Weiping Wang.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2022. CCF-B conference.Non-IID Federated Learning with Sharper Risk Bound. [pdf]
Bojian Wei, Jian Li*, Yong Liu, Weiping Wang.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. CCF-B Journal.Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth. [pdf]
Yilin Kang, Yong Liu, Jian Li, Weiping Wang.
The Conference on Information and Knowledge Management (CIKM), 2022. CCF-B conference.
2021
Federated learning for non-iid data: From theory to algorithm. [pdf] [presentation] [🏆Best student paper award]
Bojian Wei, Jian Li*, Yong Liu, Weiping Wang.
Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2021. CCF-C conference.Operation-level Progressive Differentiable Architecture Search. [pdf]
Xunyu Zhu, Jian Li*, Yong Liu, Weiping Wang.
International Conference on Data Mining (ICDM), 2021. CCF-B conference.
![[🏆Best student paper award]](https://lijian.ac.cn/files/2021/PRICAI-2021-best-student-paper.png){kind=link}
2020
Automated Spectral Kernel Learning. [pdf] [poster] [code]
Jian Li, Yong Liu, Weiping Wang.
AAAI Conference on Artificial Intelligence (AAAI), 2020. CCF-A Conference.Neural Architecture Optimization with Graph VAE. [pdf] [code]
Jian Li, Yong Liu, Weiping Wang.
arXiv preprint arXiv:2006.10310, 2020.
2019
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. [pdf] [poster] [slides] [code]
Jian Li, Yong Liu, Rong Yin, Weiping Wang.
International Joint Conference on Artificial Intelligence (IJCAI), 2019. CCF-A Conference.Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. g[pdf] [poster] [slides] [code]
Jian Li, Yong Liu, Rong Yin, Weiping Wang.
International Joint Conference on Artificial Intelligence (IJCAI), 2019. CCF-A Conference.Efficient Cross-Validation for Semi-Supervised Learning. [pdf]
Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang.
arXiv preprint arXiv:1902.04768, 2019.
2018
Multi-Class Learning: From Theory to Algorithm. [pdf] [poster] [sildes] [3-minute video] [code]
Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang.
Advances in Neural Information Processing Systems 31 (NeurIPS), 2018. CCF-A Conference.Max-Diversity Distributed Learning: Theory and Algorithms. [pdf] [code]
Yong Liu, Jian Li, Weiping Wang.
arXiv preprint arXiv:1812.07738, 2018.