Jian Li

According to our database, Jian Li authored at least 34 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Non-IID Federated Learning With Sharper Risk Bound.

Bojian Wei, Jian Li, Yong Liu, Weiping Wang

IEEE Trans. Neural Networks Learn. Syst., May, 2024

Optimal Rates for Agnostic Distributed Learning.

Jian Li, Yong Liu, Weiping Wang

IEEE Trans. Inf. Theory, April, 2024

Improving Small Language Models' Mathematical Reasoning via Equation-of-Thought Distillation.

Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang

CoRR, 2024

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning.

Jian Li, Yong Liu, Wei Wang, Haoran Wu, Weiping Wang

CoRR, 2024

2023

Improving Differentiable Architecture Search via self-distillation.

Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang

Neural Networks, October, 2023

Semi-supervised vector-valued learning: Improved bounds and algorithms.

Jian Li, Yong Liu, Weiping Wang

Pattern Recognit., June, 2023

Optimal Convergence Rates for Distributed Nystroem Approximation.

Jian Li, Yong Liu, Weiping Wang

J. Mach. Learn. Res., 2023

A Survey on Model Compression for Large Language Models.

Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang

CoRR, 2023

Robust Neural Architecture Search.

Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang

CoRR, 2023

Operation-level Progressive Differentiable Architecture Search.

Xunyu Zhu, Jian Li, Yong Liu, Weiping Wang

CoRR, 2023

Towards Sharper Risk Bounds for Agnostic Multi-objective Learning.

Bojian Wei, Jian Li, Weiping Wang

Proceedings of the International Joint Conference on Neural Networks, 2023

Towards Sharp Analysis for Distributed Learning with Random Features.

Jian Li, Yong Liu

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Optimal Convergence Rates for Agnostic Nyström Kernel Learning.

Jian Li, Yong Liu, Weiping Wang

Proceedings of the International Conference on Machine Learning, 2023

Data Heterogeneity Differential Privacy: From Theory to Algorithm.

Yilin Kang, Jian Li, Yong Liu, Weiping Wang

Proceedings of the Computational Science - ICCS 2023, 2023

2022

Sharper Utility Bounds for Differentially Private Models.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

CoRR, 2022

Stability and Generalization of Differentially Private Minimax Problems.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

CoRR, 2022

Convolutional spectral kernel learning with generalization guarantees.

Jian Li, Yong Liu, Weiping Wang

Artif. Intell., 2022

Non-IID Distributed Learning with Optimal Mixture Weights.

Jian Li, Bojian Wei, Yong Liu, Weiping Wang

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Ridgeless Regression with Random Features.

Jian Li, Yong Liu, Yingying Zhang

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Differential Privacy for Pairwise Learning: Non-convex Analysis.

Yilin Kang, Yong Liu, Jian Li, Weiping Wang

CoRR, 2021

Federated Learning for Non-IID Data: From Theory to Algorithm.

Bojian Wei, Jian Li, Yong Liu, Weiping Wang

Proceedings of the PRICAI 2021: Trends in Artificial Intelligence, 2021

Operation-level Progressive Differentiable Architecture Search.

Xunyu Zhu, Jian Li, Yong Liu, Jun Liao, Weiping Wang

Proceedings of the IEEE International Conference on Data Mining, 2021

2020

Neural Architecture Optimization with Graph VAE.

Jian Li, Yong Liu, Jiankun Liu, Weiping Wang

CoRR, 2020

Convolutional Spectral Kernel Learning.

Jian Li, Yong Liu, Weiping Wang

CoRR, 2020

Automated Spectral Kernel Learning.

Jian Li, Yong Liu, Weiping Wang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Learning Vector-valued Functions with Local Rademacher Complexity.

Jian Li, Yong Liu, Weiping Wang

CoRR, 2019

Distributed Learning with Random Features.

Jian Li, Yong Liu, Weiping Wang

CoRR, 2019

Efficient Cross-Validation for Semi-Supervised Learning.

Yong Liu, Jian Li, Guangjun Wu, Lizhong Ding, Weiping Wang

CoRR, 2019

Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis.

Jian Li, Yong Liu, Rong Yin, Weiping Wang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Multi-Class Learning using Unlabeled Samples: Theory and Algorithm.

Jian Li, Yong Liu, Rong Yin, Weiping Wang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Max-Diversity Distributed Learning: Theory and Algorithms.

Yong Liu, Jian Li, Weiping Wang

CoRR, 2018

Multi-Class Learning: From Theory to Algorithm.

Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Efficient Kernel Selection via Spectral Analysis.

Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017