学术成果丨基地重大项目研究成果（三）
2025-01-20
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. Li, Z., Zhang, Y., Yin, J. Estimating Double Sparse Structures over ℓu (ℓq)-Balls: Minimax Rates and Phase Transition. IEEE Transactions on Information Theory. 2024, 70:7066-7088.
2. Zhang, Y., Li, Z., Liu, S., Yin, J. A minimax optimal approach to high-dimensional double sparse linear regression. Journal of Machine Learning Research. 2024, 25(369):1−66.
3. Qiu, Y., Gao Q., Wang, X. Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks. Journal of the American Statistical Association. 2024. DOI: 10.1080/01621459.2024.2408778.
4. Wu, Y., Lan, W., Fan, X., Fang, K. Bipartite network influence analysis of a two-mode network. Journal of Econometrics. 2024, 239:105562.
5. Su, W., Guo, X., Chang, X., Yang, Y. Spectral Co-Clustering in Multi-layer Directed Networks. Computational Statistics & Data Analysis. 2024, 198:107987.
6. Guo, X., Li, X., Chang, X. Wang, S., Zhang, Z. Fedpower: Privacy-Preserving Distributed Eigenspace Estimation. Machine Learning. 2024. 113: 8427–8458.
论文题目与摘要
1. Li, Z., Zhang, Y., Yin, J. Estimating Double Sparse Structures over ℓu (ℓq)-Balls: Minimax Rates and Phase Transition. IEEE Transactions on Information Theory. 2024, 70:7066-7088.
https://ieeexplore.ieee.org/document/10659134
2. Zhang, Y., Li, Z., Liu, S., Yin, J. A minimax optimal approach to high-dimensional double sparse linear regression. Journal of Machine Learning Research. 2024, Online.
https://jmlr.org/papers/v25/23-0653.html
3. Qiu, Y., Gao Q., Wang, X. Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks. Journal of the American Statistical Association. 2024. DOI: 10.1080/01621459.2024.2408778.
https://www.tandfonline.com/doi/full/10.1080/01621459.2024.2408778
4. Wu, Y., Lan, W., Fan, X., Fang, K. Bipartite network influence analysis of a two-mode network. Journal of Econometrics. 2024, 239:105562.
https://www.sciencedirect.com/science/article/abs/pii/S0304407623002786
5. Su, W., Guo, X., Chang, X., Yang, Y. Spectral Co-Clustering in Multi-layer Directed Networks. Computational Statistics & Data Analysis. 2024, 198:107987.
https://www.sciencedirect.com/science/article/pii/S0167947324000719
6. Guo, X., Li, X., Chang, X. Wang, S., Zhang, Z. Fedpower: Privacy-Preserving Distributed Eigenspace Estimation. Machine Learning. 2024. 113: 8427–8458.
https://link.springer.com/article/10.1007/s10994-024-06620-0
