Software
- [changepoints] an R package containing several methods for change point localization.
Publications
Selected Publications
(* indicates student collaborators)
Distribution Learning and Generative Models
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Peng, Y.*; Yang, S.; Khoo, Y.; Wang, D. (2025).
Tensor Density Estimator by Convolution‐Deconvolution.
Preprint: [pdf] -
Peng, Y.*; Khoo, Y.; Wang, D. (2024).
Nonparametric estimation via variance‐reduced sketching.
Preprint: [pdf] -
Wang, D.; Lu, X.; Rinaldo, A. (2019).
DBSCAN: Optimal rates for density based clustering.
Journal of Machine Learning Research, 20.170:1-50. [pdf] -
Ciollaro, M.; Genovese, C.; Wang, D. (2016).
Nonparametric clustering of functional data using pseudo‐densities.
Electronic Journal of Statistics, 10.2:2922-2972. [pdf]
Supervised Learning
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Padilla, C.M.M.*; Padilla, O.H.M.; Wang, D. (2024).
Temporal‐spatial model via Trend Filtering.
Preprint: [pdf] -
Zhang, Z.; Padilla, C.M.M.*; Wang, D.; Padilla, O.H.M. (2024).
Dense ReLU neural networks for temporal‐spatial model.
Preprint: [pdf] -
Wang, D.; Zhao, Z.; Yu, Y.; Willett, R. (2022).
Functional linear regression with mixed predictors.
Journal of Machine Learning Research, to appear. [pdf] [R code]
Change Point Detection
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Xu, H.*; Wang, D.; Zhao, Z.; Yi, Y. (2024).
Change point inference in high‐dimensional regression models under temporal dependence.
Annals of Statistics, 52.3: 999-1026. [pdf] [R package] -
Padilla, C.M.M.*; Xu, H.; Wang, D.; Padilla, O.H.M.; Yu, Y. (2023).
Change point detection and inference in multivariable nonparametric models under mixing conditions.
NeurIPS 2023. [pdf] -
Wang, D.; Yu, Y.; Rinaldo, A. (2021).
Optimal change point detection and localization in sparse dynamic networks.
Annals of Statistics, 49.1:203-232. [pdf] [R package] -
Wang, D.; Yu, Y.; Rinaldo, A. (2020).
Univariate mean change point detection: Penalization, CUSUM and optimality.
Electronic Journal of Statistics, 14.1:1917-1961. [pdf] [R package]