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Pengkun Yang
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2020 – today
- 2024
- [j7]Lili Su, Jiaming Xu, Pengkun Yang:
Global Convergence of Federated Learning for Mixed Regression. IEEE Trans. Inf. Theory 70(9): 6391-6411 (2024) - [c10]Xingjian Li, Pengkun Yang, Yangcheng Gu, Xueying Zhan, Tianyang Wang, Min Xu, Chengzhong Xu:
Deep Active Learning with Noise Stability. AAAI 2024: 13655-13663 - [c9]Dong Huang, Xianwen Song, Pengkun Yang:
Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs. COLT 2024: 2494-2518 - [i16]Yun Ma, Yihong Wu, Pengkun Yang:
On the best approximation by finite Gaussian mixtures. CoRR abs/2404.08913 (2024) - [i15]Dong Huang, Xianwen Song, Pengkun Yang:
Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs. CoRR abs/2406.05428 (2024) - 2023
- [j6]Yuchao Chen, Haoyue Tang, Jintao Wang, Pengkun Yang, Leandros Tassiulas:
Sampling for remote estimation of an Ornstein-Uhlenbeck process through channel with unknown delay statistics. J. Commun. Networks 25(5): 670-687 (2023) - [j5]Lili Su, Jiaming Xu, Pengkun Yang:
A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points. J. Mach. Learn. Res. 24: 203:1-203:48 (2023) - [j4]Xingjian Li
, Abulikemu Abuduweili
, Humphrey Shi
, Pengkun Yang, Dejing Dou, Haoyi Xiong, Chengzhong Xu:
Semi-supervised transfer learning with hierarchical self-regularization. Pattern Recognit. 144: 109831 (2023) - [j3]Haoyue Tang
, Yuchao Chen
, Jintao Wang
, Pengkun Yang
, Leandros Tassiulas:
Age Optimal Sampling Under Unknown Delay Statistics. IEEE Trans. Inf. Theory 69(2): 1295-1314 (2023) - [c8]Yun Ma, Yihong Wu, Pengkun Yang:
On the best approximation by finite Gaussian mixtures. ISIT 2023: 2619-2624 - [i14]Lili Su, Jiaming Xu, Pengkun Yang:
Federated Learning in the Presence of Adversarial Client Unavailability. CoRR abs/2305.19971 (2023) - [i13]Pengkun Yang, Jingzhao Zhang:
Two Phases of Scaling Laws for Nearest Neighbor Classifiers. CoRR abs/2308.08247 (2023) - [i12]Yuchao Chen, Haoyue Tang, Jintao Wang, Pengkun Yang, Leandros Tassiulas:
Sampling for Remote Estimation of an Ornstein-Uhlenbeck Process through Channel with Unknown Delay Statistics. CoRR abs/2308.15401 (2023) - 2022
- [c7]Tianyang Wang, Xingjian Li, Pengkun Yang, Guosheng Hu, Xiangrui Zeng, Siyu Huang
, Cheng-Zhong Xu, Min Xu
:
Boosting Active Learning via Improving Test Performance. AAAI 2022: 8566-8574 - [c6]Lili Su, Jiaming Xu, Pengkun Yang:
Global Convergence of Federated Learning for Mixed Regression. NeurIPS 2022 - [i11]Haoyue Tang, Yuchao Chen, Jintao Wang, Pengkun Yang, Leandros Tassiulas:
Age Optimal Sampling Under Unknown Delay Statistics. CoRR abs/2202.13367 (2022) - [i10]Xingjian Li, Pengkun Yang, Tianyang Wang, Xueying Zhan, Min Xu, Dejing Dou, Chengzhong Xu:
Deep Active Learning with Noise Stability. CoRR abs/2205.13340 (2022) - [i9]Lili Su, Jiaming Xu, Pengkun Yang:
Global Convergence of Federated Learning for Mixed Regression. CoRR abs/2206.07279 (2022) - 2021
- [c5]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. COLT 2021: 1887-1936 - [i8]Yihong Wu, Pengkun Yang:
Polynomial methods in statistical inference: theory and practice. CoRR abs/2104.07317 (2021) - [i7]Lili Su, Jiaming Xu, Pengkun Yang:
Achieving Statistical Optimality of Federated Learning: Beyond Stationary Points. CoRR abs/2106.15216 (2021) - [i6]Tianyang Wang, Xingjian Li, Pengkun Yang, Guosheng Hu, Xiangrui Zeng, Siyu Huang, Cheng-Zhong Xu, Min Xu:
Boosting Active Learning via Improving Test Performance. CoRR abs/2112.05683 (2021) - 2020
- [j2]Yihong Wu, Pengkun Yang:
Polynomial Methods in Statistical Inference: Theory and Practice. Found. Trends Commun. Inf. Theory 17(4): 402-586 (2020) - [i5]Natalie Doss, Yihong Wu, Pengkun Yang, Harrison H. Zhou:
Optimal estimation of high-dimensional Gaussian mixtures. CoRR abs/2002.05818 (2020) - [i4]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. CoRR abs/2007.01452 (2020)
2010 – 2019
- 2019
- [c4]Lili Su, Pengkun Yang:
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective. NeurIPS 2019: 2637-2646 - [i3]Lili Su, Pengkun Yang:
On Learning Over-parameterized Neural Networks: A Functional Approximation Prospective. CoRR abs/1905.10826 (2019) - 2018
- [b1]Pengkun Yang:
Polynomial methods in statistical inference: Theory and practice. University of Illinois Urbana-Champaign, USA, 2018 - 2016
- [j1]Yihong Wu, Pengkun Yang:
Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation. IEEE Trans. Inf. Theory 62(6): 3702-3720 (2016) - 2015
- [c3]Yihong Wu, Pengkun Yang:
Optimal entropy estimation on large alphabets via best polynomial approximation. ISIT 2015: 824-828 - 2014
- [i2]Yihong Wu, Pengkun Yang:
Minimax rates of entropy estimation on large alphabets via best polynomial approximation. CoRR abs/1407.0381 (2014) - 2013
- [c2]Jiaxin Cao, Rui Xia, Pengkun Yang, Chuanxiong Guo
, Guohan Lu, Lihua Yuan, Yixin Zheng, Haitao Wu, Yongqiang Xiong, David A. Maltz:
Per-packet load-balanced, low-latency routing for clos-based data center networks. CoNEXT 2013: 49-60 - [c1]Tao Zhao
, Pengkun Yang, Huimin Pan, Ruichen Deng, Sheng Zhou, Zhisheng Niu:
Software defined radio implementation of signaling splitting in hyper-cellular network. SRIF@SIGCOMM 2013: 81-84 - [i1]Tao Zhao, Pengkun Yang, Huimin Pan, Ruichen Deng, Sheng Zhou, Zhisheng Niu:
Software Defined Radio Implementation of Signaling Splitting in Hyper-Cellular Network. CoRR abs/1312.0720 (2013)
Coauthor Index
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last updated on 2024-09-05 23:43 CEST by the dblp team
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