2024 ICML Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms Ye Tian, Haolei Weng, and Yang Feng International Conference on Machine Learning, 2024 arXiv Bib @article{tian2024unsupervised, title = {Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms}, author = {Tian, Ye and Weng, Haolei and Feng, Yang}, journal = {International Conference on Machine Learning}, year = {2024}, eprint = {2310.15330}, archiveprefix = {arXiv}, primaryclass = {stat.ML}, } Preprint Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data Daewoo Pak, Jianrui Zhang, Di Wu, Haolei Weng, and Chenxi Li 2024 arXiv Bib @misc{pak2024varibleselect, title = {Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data}, author = {Pak, Daewoo and Zhang, Jianrui and Wu, Di and Weng, Haolei and Li, Chenxi}, year = {2024}, eprint = {2405.01275}, archiveprefix = {arXiv}, primaryclass = {stat.ME}, } Preprint A note on the minimax risk of sparse linear regression Yilin Guo, Shubhangi Ghosh, Haolei Weng, and Arian Maleki 2024 arXiv Bib @misc{guo2024minimaxlr, title = {A note on the minimax risk of sparse linear regression}, author = {Guo, Yilin and Ghosh, Shubhangi and Weng, Haolei and Maleki, Arian}, year = {2024}, eprint = {2405.05344}, archiveprefix = {arXiv}, primaryclass = {stat.ME}, } Preprint Robust penalized least squares of depth trimmed regression Yijun Zuo, Pingshou Zhong, and Haolei Weng 2024 HTML 2023 JCGS Spectral clustering via adaptive layer aggregation for multi-layer networks Sihan Huang, Haolei Weng, and Yang Feng Journal of Computational and Graphical Statistics, 2023 arXiv Bib HTML @article{huang2023spectral, title = {Spectral clustering via adaptive layer aggregation for multi-layer networks}, author = {Huang, Sihan and Weng, Haolei and Feng, Yang}, journal = {Journal of Computational and Graphical Statistics}, volume = {32}, number = {3}, pages = {1170--1184}, year = {2023}, publisher = {Taylor \& Francis}, } JRSSA Variable Selection in Latent Regression IRT Models via Knockoffs: An Application to International Large-scale Assessment in Education Zilong Xie, Yunxiao Chen, Matthias von Davier, and Haolei Weng Journal of the Royal Statistical Society: Series A, 2023 arXiv Bib HTML @article{xie2023variable, title = {Variable Selection in Latent Regression IRT Models via Knockoffs: An Application to International Large-scale Assessment in Education}, author = {Xie, Zilong and Chen, Yunxiao and {von Davier}, Matthias and Weng, Haolei}, journal = {Journal of the Royal Statistical Society: Series A}, year = {2023}, eprint = {2208.07959}, archiveprefix = {arXiv}, primaryclass = {stat.ME}, } IEEE Trans. Inf. Signal-to-noise ratio aware minimaxity and higher-order asymptotics Yilin Guo, Haolei Weng, and Arian Maleki IEEE Transactions on Information Theory, 2023 arXiv Bib @article{guo2022signaltonoise, title = {Signal-to-noise ratio aware minimaxity and higher-order asymptotics}, author = {Guo, Yilin and Weng, Haolei and Maleki, Arian}, journal = {IEEE Transactions on Information Theory}, year = {2023}, eprint = {2211.05954}, archiveprefix = {arXiv}, primaryclass = {math.ST}, } Preprint Post-Selection Inference for the Cox Model with Interval-Censored Data Jianrui Zhang, Chenxi Li, and Haolei Weng 2023 arXiv Bib @misc{zhang2023postselection, title = {Post-Selection Inference for the Cox Model with Interval-Censored Data}, author = {Zhang, Jianrui and Li, Chenxi and Weng, Haolei}, year = {2023}, eprint = {2306.13870}, archiveprefix = {arXiv}, primaryclass = {stat.ME}, } 2022 Inf. Inference Does SLOPE outperform bridge regression? Shuaiwen Wang, Haolei Weng, and Arian Maleki Information and Inference: A Journal of the IMA, 2022 arXiv Bib HTML @article{wang2022does, title = {Does SLOPE outperform bridge regression?}, author = {Wang, Shuaiwen and Weng, Haolei and Maleki, Arian}, journal = {Information and Inference: A Journal of the IMA}, volume = {11}, number = {1}, pages = {1--54}, year = {2022}, publisher = {Oxford University Press}, } Stat Community detection with nodal information: Likelihood and its variational approximation Haolei Weng, and Yang Feng Stat, 2022 Bib HTML @article{weng2022community, title = {Community detection with nodal information: Likelihood and its variational approximation}, author = {Weng, Haolei and Feng, Yang}, journal = {Stat}, volume = {11}, number = {1}, pages = {e428}, year = {2022}, publisher = {Wiley Online Library}, } JBES Discussion of “Cocitation and Coauthorship Networks of Statisticians” Haolei Weng, and Yang Feng Journal of Business & Economic Statistics, 2022 Bib HTML @article{weng2022discuss, title = {Discussion of “Cocitation and Coauthorship Networks of Statisticians”}, author = {Weng, Haolei and Feng, Yang}, journal = {Journal of Business \& Economic Statistics}, volume = {40}, number = {2}, pages = {486--490}, year = {2022}, publisher = {Wiley Online Library}, } Preprint Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models Ye Tian, Haolei Weng, and Yang Feng 2022 arXiv Bib @misc{tian2022unsupervised, title = {Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models}, author = {Tian, Ye and Weng, Haolei and Feng, Yang}, year = {2022}, eprint = {2209.15224}, archiveprefix = {arXiv}, primaryclass = {stat.ML}, } 2021 Preprint Optimal estimation of functionals of high-dimensional mean and covariance matrix Jianqing Fan, Haolei Weng, and Yifeng Zhou 2021 arXiv Bib @misc{fan2021optimal, title = {Optimal estimation of functionals of high-dimensional mean and covariance matrix}, author = {Fan, Jianqing and Weng, Haolei and Zhou, Yifeng}, year = {2021}, eprint = {1908.07460}, archiveprefix = {arXiv}, primaryclass = {math.ST}, } 2020 Inf. Inference Low noise sensitivity analysis of ℓq-minimization in oversampled systems Haolei Weng, and Arian Maleki Information and Inference: A Journal of the IMA, 2020 Bib HTML @article{weng2020low, title = {Low noise sensitivity analysis of ℓq-minimization in oversampled systems}, author = {Weng, Haolei and Maleki, Arian}, journal = {Information and Inference: A Journal of the IMA}, volume = {9}, number = {1}, pages = {113--155}, year = {2020}, publisher = {Oxford University Press}, } JSPI On the estimation of correlation in a binary sequence model Haolei Weng, and Yang Feng Journal of Statistical Planning and Inference, 2020 Bib HTML @article{weng2020estimation, title = {On the estimation of correlation in a binary sequence model}, author = {Weng, Haolei and Feng, Yang}, journal = {Journal of Statistical Planning and Inference}, volume = {207}, pages = {123--137}, year = {2020}, publisher = {Elsevier}, } EJS Computing the degrees of freedom of rank-regularized estimators and cousins Rahul Mazumder, and Haolei Weng Electronic Journal of Statistics, 2020 Bib HTML @article{mazumder2020computing, title = {Computing the degrees of freedom of rank-regularized estimators and cousins}, author = {Mazumder, Rahul and Weng, Haolei}, journal = {Electronic Journal of Statistics}, volume = {14}, pages = {1348--1385}, year = {2020}, } Stat. Comput. Matrix completion with nonconvex regularization: spectral operators and scalable algorithms Rahul Mazumder, Diego Saldana, and Haolei Weng Statistics and Computing, 2020 Bib HTML @article{mazumder2020matrix, title = {Matrix completion with nonconvex regularization: spectral operators and scalable algorithms}, author = {Mazumder, Rahul and Saldana, Diego and Weng, Haolei}, journal = {Statistics and Computing}, volume = {30}, number = {4}, pages = {1113--1138}, year = {2020}, publisher = {Springer}, } Ann. Stat. Which bridge estimator is the best for variable selection? Shuaiwen Wang, Haolei Weng, and Arian Maleki The Annals of Statistics, 2020 Bib HTML @article{wang2020bridge, title = {Which bridge estimator is the best for variable selection?}, author = {Wang, Shuaiwen and Weng, Haolei and Maleki, Arian}, journal = {The Annals of Statistics}, volume = {48}, number = {5}, pages = {2791--2823}, year = {2020}, publisher = {Institute of Mathematical Statistics}, } 2019 Stat. Sin. Regularization after retention in ultrahigh dimensional linear regression models Haolei Weng, Yang Feng, and Xingye Qiao Statistica Sinica, 2019 Bib HTML @article{weng2019regularization, title = {Regularization after retention in ultrahigh dimensional linear regression models}, author = {Weng, Haolei and Feng, Yang and Qiao, Xingye}, journal = {Statistica Sinica}, volume = {29}, number = {1}, pages = {387--407}, year = {2019}, publisher = {JSTOR}, } 2018 Ann. Stat. Overcoming the limitations of phase transition by higher order analysis of regularization techniques Haolei Weng, Arian Maleki, and Le Zheng The Annals of Statistics, 2018 Bib HTML @article{weng2018overcoming, title = {Overcoming the limitations of phase transition by higher order analysis of regularization techniques}, author = {Weng, Haolei and Maleki, Arian and Zheng, Le}, journal = {The Annals of Statistics}, volume = {46}, number = {6A}, pages = {3099--3129}, year = {2018}, publisher = {Institute of Mathematical Statistics}, } 2017 IEEE Trans. Inf. Does ℓp-minimization outperform ℓ1-minimization? Le Zheng, Arian Maleki, Haolei Weng, Xiaodong Wang, and Teng Long IEEE Transactions on Information Theory, 2017 Bib HTML @article{zheng2017does, title = {Does ℓp-minimization outperform ℓ1-minimization?}, author = {Zheng, Le and Maleki, Arian and Weng, Haolei and Wang, Xiaodong and Long, Teng}, journal = {IEEE Transactions on Information Theory}, volume = {63}, number = {11}, pages = {6896--6935}, year = {2017}, publisher = {IEEE}, } 2016 ISIT Phase transition and noise sensitivity of ℓp-minimization for 0 ≤p ≤1 Haolei Weng, Le Zheng, Arian Maleki, and Xiaodong Wang In 2016 IEEE International Symposium on Information Theory (ISIT), 2016 Bib HTML @inproceedings{weng2016phase, title = {Phase transition and noise sensitivity of ℓp-minimization for $0 \leq p \leq 1$}, author = {Weng, Haolei and Zheng, Le and Maleki, Arian and Wang, Xiaodong}, booktitle = {2016 IEEE International Symposium on Information Theory (ISIT)}, pages = {675--679}, year = {2016}, organization = {IEEE}, }