Zuofeng Shang

Xin Xing, Zuofeng Shang, Pang Du, Ping Ma, Wenxuan Zhong, Jun S Liu. 2024. “Minimax Nonparametric Multi-sample Test under Smoothing.” Statistica Sinica.

Shuoyang Wang, Guanqun Cao, Zuofeng Shang. 2023. “Deep Neural Network Classifier for Multi-dimensional Functional Data.” Scandinavian Journal of Statistics.

Ruiqi Liu, Ganggang Xu, Zuofeng Shang. 2023. “Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory.” Statistics and Computing.

Hewei Zhang, Qin Li, Yixin Yang, Ju Jing, Jason T. Wang, Haimin Wang, Zuofeng Shang. 2022. “Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods.” ApJ, vol. 263, pp. 28.

Mingao Yuan, Yang Feng, Zuofeng Shang. 2022. “A likelihood-ratio type test for stochastic block models with bounded degrees.” Journal of Statistical Planning and Inference.

Hewei Zhang, Qin Li, Yanxing Yang, Jing Ju, Jason Wang, Haimin Wang, Zuofeng Shang. 2022. “Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods.” The Astrophysical Journal Supplement.

Shuoyang Wang, Zuofeng Shang. 2022. “Minimax Optimal High-Dimensional Classification using Deep Neural Networks.” Stat.

Xiaotian Dai, Guifang Fu, Randall Reese, Shaofei Zhao, Zuofeng Shang. 2022. “An Approach of Bayesian Variable Selection for Ultrahigh Dimensional Multivariate Regression.” STAT.

Ruiqi Liu, Mingao Yuan, Zuofeng Shang. 2022. “ Online statistical inference for parameters estimation with linear-equality constraints   .” Journal of Multivariate Analysis   .

Shuoyang Wang, Guanqun Cao, Zuofeng Shang. 2021. “Estimation of the Mean Function of Functional Data via Deep Neural Networks.” Stat.

Mingao Yuan, Fan Emily Yang, Zuofeng Shang. 2021. “Hypothesis Testing in Sparse Weighted Stochastic Block Model.” Statistical Papers.

Mingao Yuan, Zuofeng Shang. 2021. “Information Limits for Detecting a Subhypergraph.” STAT.

Mingao Yuan, Zuofeng Shang. 2021. “Sharp Detection Boundaries on Testing Dense Subhypergraph.” Bernoulli.

Mingao Yuan, Ruiqi Liu, Yang Feng, Zuofeng Shang. 2021. “Testing Community Structures for Hypergraph Models.” Annals of Statistics.

Ruiqi Liu, Ben Boukai, Zuofeng Shang. 2021. “Optimal Nonparametric Inference via Deep Neural Network.” Journal of Mathematical Analysis and Applications.

Meimei Liu, Zuofeng Shang, Yun Yang, Guang Cheng. 2021. “Nonparametric Testing under Randomized Sketching.” IEEE Transactions on Pattern Analysis and Machine Intelligence.

Meimei Liu, Zuofeng Shang, Guang Cheng. 2020. “Nonparametric Distributed Learning under Random Design.” Electronic Journal of Statistics.

Ruiqi Liu, Zuofeng Shang, Yonghui Zhang, Qiankun Zhou. 2020. “Identification and estimation in panel models with overspecified number of groups.” Journal of Econometrics.

Ganggang Xu, Zuofeng Shang, Guang Cheng. 2019. “Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and its Asymptotic Optimality.” Journal of Computational and Graphical Statistics.

Zuofeng Shang, Botao Hao, Guang Cheng. 2019. “Nonparametric Bayesian Aggregation for Massive Data.” Journal of Machine Learning Research.

Shunan Zhao, Ruiqi Liu, Zuofeng Shang. 2019. “Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method.” Journal of Business & Economic Statistics .

“Core Matrix Regression and Prediction with Regularization”
November 2022.

“Temporal Bipartite Graph Neural Networks for Bond Prediction”
November 2022.

“Temporal Bipartite Graph Neural Networks for Predicting Financial Time Series with Irregular Intervals”
ICDM/SSTDM 2022 : 17th International Workshop on Spatial and Spatiotemporal Data Mining, October (4th Quarter/Autumn) 2022.

“ Non-asymptotic Theory for Nonparametric Testing”
33rd Annual Conference on Learning Theory, 2020.

“Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks”
The International Conference on Learning Representations (ICLR) 2020, 2020.

“Sharp Theoretical Analysis for Nonparametric Testing under Random Projection”
32nd Annual Conference on Learning Theory, 2019.