Home > PEOPLE > Faculty > Content

Pengkun Yang Associate Professor

Research Areas:theory and algorithms for high-dimensional statistics, mathematical data science, statistical machine learning, optimization

Office: Room 816, Lyu Dalong Building, Tsinghua University


Phone: +86-10-62787138


Email: yangpengkun@tsinghua.edu.cn


职称 Associate Professor 地址 Room 816, Lyu Dalong Building, Tsinghua University
电话 +86-10-62787138 邮箱 yangpengkun@tsinghua.edu.cn
个人主页

Work Experiences:

Education:

Monographs:

Journal publications:

  • Y. Ma, Y. Wu, P. Yang, (α-β) “On the best approximation by finite Gaussian mixtures”, IEEE Transactions on Information Theory, 2025.

  • L. Su, J. Xu, P. Yang, (α-β) “Global Convergence of Federated Learning for Mixed Regression”, IEEE Transactions on Information Theory, 2024.

  • Y. Chen, J. Wang, H. Tang, P. Yang, L. Tassiulas, “Sampling for Remote Estimation of an Ornstein-Uhlenbeck Process through Channel with Un- known Delay Statistics,” Journal of Communications and Networks, 2023.

  • X. Li, A. Abuduweili, H. Shi, P. Yang, H. Xiong, C. Xu, D. Dou, “Semi-supervised Transfer Learning with Hierarchical Self-regularization,” Pattern Recognition, Volume 144, 109831, 2023.

  • L. Su, J. Xu, P. Yang, (α-β) “A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points,” Journal of Machine Learning Research, Volume 24, Issue 203, pp. 1-48, 2023.

  • H. Tang, Y. Chen, J. Wang, P. Yang, L. Tassiulas, “Age Optimal Sampling Under Unknown Delay Statistics,” IEEE Transactions on Information Theory, Volume 69, Issue 2, pp. 1295-1314, 2023.

  • N. Doss, Y. Wu, P. Yang, and H. H. Zhou, (α-β) “Optimal estimation of high-dimensional Gaussian location mixtures,” Annals of Statistics, Volume 51, Issue 1, pp. 62-95, 2023.

  • Y. Wu, P. Yang, (α-β) “Optimal estimation of Gaussian mixtures via denoised method of moments,” Annals of Statistics, Volume 48, Number 4, pp. 1981-2007, 2020.

  • D. Tao, P. Yang, H. Feng, “Utilization of text mining as a big data analysis tool for food science and nutrition,” Comprehensive Reviews in Food Science and Food Safety, Volume 19, Number 2, pp. 875-894, 2020.

  • Y. Wu, P. Yang, (α-β) “Chebyshev polynomials, moment matching, and optimal estimation of the unseen,” Annals of Statistics, Volume 47, Number 2, pp. 857-883, 2019.

  • Y. Wu, P. Yang, (α-β) “Sample complexity of the distinct elements problem,” Mathematical Statistics and Learning, Volume 1, Issue 1, pp. 37-72, 2018.

  • Y. Wu, P. Yang, (α-β) “Minimax rates of entropy estimation on large alphabets via best polynomial approximation,” IEEE Transactions on Information Theory, Volume 62, Issue 6, pp. 3702–3720, 2016.

Conference publications: 

  • X. Li, P. Yang (co-first), T. Wang, X. Zhan, M. Xu, D. Dou, C. Xu, “Deep Active Learning with Noise Stability”, AAAI Conference on Artificial Intelligence (AAAI), 2024.

  • D. Huang, X. Song, P. Yang, (α-β), “Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs”, Conference on Learning Theory (COLT) 2024.

  • Z. Tong, S. Cheung, Z. Ren, P. Yang, Q. Shao, “Modeling of Multi-Level Spin-Orbit Torque-MRAM: Scalability, Stochasticity, and Variations,” IEEE International Magnetic Conference, 2023.

  • Y. Ma, Y. Wu, P. Yang, (α-β) “On the best approximation by finite Gaussian mixtures,” IEEE International Symposium on Information Theory (ISIT), 2023.

  • L. Su, J. Xu, P. Yang, (α-β) “Global Convergence of Federated Learning for Mixed Regression,” Advances in Neural Information Processing Systems (NeurIPS), 2022.

  • T. Wang, X. Li, P. Yang, G. Hu, X. Zeng, S. Huang, C. Xu, M. Xu, “Boosting active learning via improving test performance,”AAAI Conference on Artificial Intelligence (AAAI), 2022.

  • C. Fang, J. Lee, P. Yang, T. Zhang, (α-β) “Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Net-works,” Conference on Learning Theory (COLT), 2021.

  • L. Su, P. Yang, (α-β) “On learning over-parameterized neural networks: a functional approximation perspective,” Advances in Neural Information Processing Systems (NeurIPS), 2019.

  • Y. Wu, P. Yang, (α-β) “Optimal entropy estimation on large alphabets via best polynomial approximation,” IEEE International Symposium on Information Theory (ISIT), 2015. Jack Keil Wolf ISIT Student Paper Award

  • T. Zhao, P. Yang, H. Pan, R. Deng, S. Zhou, Z. Niu, “Software defined radio implementation of signaling splitting in hyper-cellular network,” Second workshop on Software radio implementation forum (SRIF), ACM SIGCOMM, 2013.

  • J. Cao, R. Xia, P. Yang, C. Guo, G. Lu, L. Yuan, Y. Zheng, H. Wu, Y. Xiong, D. Maltz, “Per-packet load-balanced, low-latency routing for clos-based data center networks,”ACM conference on Emerging networking experiments and technologies (CoNEXT), 2013.

Awards:

  • Thomas M. Cover Dissertation Award, 2020 IEEE Information Theory Society

  • Shun Lien Chuang Memorial Award for Excellence in Graduate Education, 2018 University of Illinois at Urbana-Champaign

  • Jack Keil Wolf ISIT Student Paper Award, 2015 IEEE International Symposium on Information Theory (ISIT)

  • Star of Tomorrow Internship Award of Excellence, 2013 Microsoft Research Asia (MSRA)

CV_py