学术成果丨基地重大项目研究成果（五）
2025-02-17
在数字时代，数据科学已成为推动社会进步的关键力量。作为多学科融合的核心，数据科学的基础理论研究重要性日益凸显。统计学作为数据科学的核心方法论，其理论与方法的创新与突破，对于提升我国数据科学和数据技术的整体实力具有重要意义。为应对数字时代统计学中的重大基础理论与实践应用问题，本基地重大项目“数字时代的统计学理论与方法研究”利用大数据和人工智能等先进方法与工具，聚焦统计机器学习模型、高维稀疏数据、网络结构数据以及时空大数据等领域的若干关键问题开展深入研究。以下是项目组近期取得的一些研究成果：
1. Han, Y., Li, X., Lin, S., Zhang, Z. A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness. Journal of Machine Learning Research. 2024, Online.
2. Peng, Y., Zhang, L., Zhang, Z. Statistical Efficiency of Distributional Temporal Difference Learning. The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). Online.
3. Zhao, Y., Zhao, K., Chen, Z., Zhang, Y., Du, Y., Lu, X. A Graph-based Representation Framework for Trajectory Recovery via Spatiotemporal Interval-Informed Seq2Seq. The Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24). Online.
4. Di, H., Ye, H., Chang, X., Dai, G., Tsang, I. Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods. The Forty-First International Conference on Machine Learning. Online.
5. 褚挺进,华雨臻,丁一鸣,尹建鑫.高维地理空间回归模型的惩罚似然估计与模型选择.数理统计与管理. 2024, 43:407-422.
论文题目与摘要
1. Han, Y., Li, X., Lin, S., Zhang, Z. A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness. Journal of Machine Learning Research. 2024, Online.
2. Peng, Y., Zhang, L., Zhang, Z. Statistical Efficiency of Distributional Temporal Difference Learning. The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). Online.
https://neurips.cc/virtual/2024/oral/97962
3. Zhao, Y., Zhao, K., Chen, Z., Zhang, Y., Du, Y., Lu, X. A Graph-based Representation Framework for Trajectory Recovery via Spatiotemporal Interval-Informed Seq2Seq. The Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24). Online.
https://www.ijcai.org/proceedings/2024/0286.pdf
4. Di, H., Ye, H., Chang, X., Dai, G., Tsang, I. Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods. The Forty-First International Conference on Machine Learning. Online.
5. 褚挺进,华雨臻,丁一鸣,尹建鑫.高维地理空间回归模型的惩罚似然估计与模型选择.数理统计与管理. 2024, 43:407-422.
http://tongji.llyj.net/lunwen/itemid-256970.shtml
