Most of my change point analysis papers have methods implemented in the R package changepoints. This package is mainly written by Haotian Xu.

Preprints

1. Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints. (2024) arXiv preprint. [pdf]
   

   Zifeng Zhao, Feiyu Jiang and Y.
   

2. Rate Optimality and Phase Transition for User-Level Local Differential Privacy. (2024) arXiv preprint. [pdf]
   

   Alexander Kent, Thomas B. Berrett and Y.
   

3. Change point localisation and inference in fragmented functional data. (2024) arXiv preprint. [pdf]
   

   Gengyu Xue, Haotian Xu and Y.
   

4. Federated Transfer Learning with Differential Privacy. (2024) arXiv preprint. [pdf]
   

   Mengchu Li, Ye Tian, Yang Feng and Y.
   

5. Transfer learning for piecewise-constant mean estimation: Optimality, ell_1- and ell_0-penalisation. (2023) arXiv preprint. [pdf]
   

   Fan Wang and Y.
   

6. Multilayer random dot product graphs: Estimation and online change point detection. (2023) arXiv preprint. [pdf]
   

   Fan Wang, Wanshan Li, Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo
   

7. Robust mean change point testing in high-dimensional data with heavy tails. (2023) arXiv preprint. [pdf]
   

   Mengchu Li, Yudong Chen, Tengyao Wang and Y.
   

8. High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection. (2023) arXiv preprint. [pdf]
   

   Zifeng Zhao, Feiyu Jiang, Y. and Xi Chen
   

9. Dynamic and heterogeneous treatment effects with abrupt changes. (2022) arXiv preprint. [pdf]
   

   Oscar Hernan Madrid Padilla and Y.
   

10. Online network change point detection with missing values. (2021) arXiv preprint. [pdf]
    

    Paromita Dubey, Haotian Xu and Y.
    

11. Generalized non-stationary bandits. (2021) arXiv preprint. [pdf]
    

    Anne Gael Manegueu, Alexandra Carpentier and Y.
    

12. A review on minimax rates in change point detection and localisation. (2020) arXiv preprint. [pdf]
    

    Y.
    

13. Localizing changes in high-dimensional vector autoregressive processes. (2019) arXiv preprint. [pdf]
    

    Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett
    

Publications

1. Quickest Detection in High-Dimensional Linear Regression Models via Implicit Regularization. (2024) IEEE International Symposium on Information Theory.
   

   Qunzhi Xu, Y. and Yajun Mei.
   

2. Change point inference in high-dimensional regression models under temporal dependence. (2024) Annals of Statistics, to appear. [pdf]
   

   Haotian Xu, Daren Wang, Zifeng Zhao and Y.
   

3. Optimal network online change point localisation. (2023) SIAM Journal on Mathematics of Data Science, to appear. [pdf]
   

   Y., Oscar Hernan Madrid Padilla, Daren Wang and Alessandro Rinaldo
   

4. A Note on Online Change Point Detection. (2023) Sequential Analysis, to appear. DOI: 10.1080/07474946.2023.2276170. [pdf]
   

   Y., Oscar Hernan Madrid Padilla, Daren Wang and Alessandro Rinaldo
   

5. Change point detection and inference in multivariable nonparametric models under mixing conditions. (2023) NeurIPS. [pdf]
   

   Carlos Misael Madrid Padilla, Haotian Xu, Daren Wang, Oscar Hernan Madrid Padilla and Y.
   

6. On robustness and local differential privacy. (2023) Annals of Statistics, Vol. 51, No. 2, 717-737. [pdf]
   

   Mengchu Li, Thomas B. Berrett and Y.
   

7. Change-point Detection for Sparse and Dense Functional Data in General Dimensions. (2022) NeurIPS. [pdf]
   

   Carlos Misael Madrid Padilla, Daren Wang, Zifeng Zhao and Y.
   

8. Network change point localisation under local differential privacy. (2022) NeurIPS. [pdf]
   

   Mengchu Li, Thomas B. Berrett and Y.
   

9. Change point localization in dependent dynamic nonparametric random dot product graphs. (2022) Journal of Machine Learning Research, 23(234), 1-59. [pdf]
   

   Oscar Hernan Madrid Padilla, Y. and Carey E. Priebe
   

10. Functional Linear Regression with Mixed Predictors. (2022) Journal of Machine Learning Research, 3(266), 1-94 [pdf]
    

    Daren Wang, Zifeng Zhao, Y., and Rebecca Willett
    

11. Detecting abrupt changes in high-dimensional self-exciting Poisson processes. (2022) Statistica Sinica, doi: 10.5705/ss.202021.0221. [pdf]
    

    Daren Wang, Y. and Rebecca Willett
    

12. Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. (2022) In International Conference on Artificial Intelligence and Statistics, pp. 4309–4338. PMLR. (Oral presentation). [pdf]
    

    Fan Wang, Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo
    

13. Optimal partition recovery in general graphs. (2022) In International Conference on Artificial Intelligence and Statistics, pp. 4339–4358. PMLR. [pdf]
    

    Y., Oscar Hernan Madrid Padilla and Alessandro Rinaldo
    

14. Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs. (2021) Statistics and Computing, Vol. 32, No. 1, pp. 1–16. [pdf]
    

    Yaofang Hu, Wanjie Wang and Y.
    

15. Localising change points in piecewise polynomials of general degrees. (2021) Electronic Journal of Statistics, Vol. 16, No. 1, pp. 1855–1890. [pdf]
    

    Y., Sabyasachi Chatterjee and Haotian Xu
    

16. Optimal nonparametric multivariate change point detection and localization. (2021) IEEE Transactions on Information Theory, Vol. 68, No. 3, pp. 1922–1944. [pdf]
    

    Oscar Hernan Madrid Padilla, Y., Daren Wang and Alessandro Rinaldo
    

17. Lattice partition recovery with dyadic CART. (2021) Advances in Neural Information Processing Systems, 34, 26143–26155. [pdf]
    

    Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo
    

18. Locally private online change point detection. (2021) Advances in Neural Information Processing Systems, 34, 3425-3437. [pdf]
    

    Thomas B. Berrett and Y.
    

19. Adversarially Robust Change Point Detection. (2021) Advances in Neural Information Processing Systems, 34, 22955–22967. [pdf]
    

    Mengchu Li and Y.
    

20. Optimal nonparametric change point detection and localization. (2021) Electronic Journal of Statistics, Vol. 15, No. 1, 1154-1201. [pdf]
    

    Oscar Hernan Madrid Padilla, Y., Daren Wang and Alessandro Rinaldo
    

21. Localizing changes in high-dimensional regression models. (2021) In International Conference on Artificial Intelligence and Statistics, pp. 2089–2097. PMLR. [pdf]
    

    Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett and Y.
    

22. Optimal Covariance Change Point Detection in High Dimension. (2020) Bernoulli, Vol. 27, No. 1, pp. 554–575. [pdf]
    

    Daren Wang, Y. and Alessandro Rinaldo
    

23. Univariate mean change point detection: penalization, CUSUM and optimality. (2020) Electronic Journal of Statistics, Vol. 14, No. 1, 1917--1961. [pdf]
    

    Daren Wang, Y. and Alessandro Rinaldo
    

24. Event history analysis of dynamic networks. (2020) Biometrika, Vol. 108, No. 1, pp. 223–230. [pdf]
    

    Tony Sit, Zhiliang Ying and Y.
    

25. Optimal change point detection and localization in sparse dynamic networks. (2021) Annals of Statistics, Vol. 49, No. 1, 203-232. [pdf]
    

    Daren Wang, Y. and Alessandro Rinaldo
    

26. Spectral analysis of high-dimensional time series. (2019) Electronic Journal of Statistics, Vol. 13, 4079-4101. [pdf]
    

    Mark Fiecas, Chenlei Leng, Weidong Liu and Y.
    

27. Confidence intervals for high-dimensional Cox models. (2021) Statistica Sinica, 31, 243--267. [pdf]
    

    Y., Jelena Bradic and Richard J. Samworth
    

28. Two new approaches for the visualisation of models for network meta-analysis. (2019) BMC Medical Research Methodology, Vol. 19, No. 1, pp. 1–18. [pdf]
    

    Martin Law, Navid Alam, Areti Angeliki Veroniki, Y. and Dan Jackson
    

29. Link prediction for inter-disciplinary collaboration via co-authorship network. (2018) Social Network Analysis and Mining, 8, 25. [pdf]
    

    Haeran Cho and Y.
    

30. The restricted consistency property of leave-nv-out cross-validation for high-dimensional variable selection. (2019) Statistica Sinica, 29, 1607--1630 [pdf]
    

    Yang Feng and Y.
    

31. Estimating whole brain dynamics using spectral clustering. (2017) Journal of Royal Statistical Society, Series C, 66, 607--627. [pdf]
    

    Ivor Cribben and Y.
    

32. How many communities are there? (2017) Journal of Computational and Graphical Statistics, 26, 171--181. [pdf]
    

    Diego Franco Saldana, Y. and Yang Feng

33. A useful variant of the Davis--Kahan theorem for statisticians. (2015) Biometrika, 102, 315--323. [pdf]
    

    Y., Tengyao Wang and Richard J. Samworth
    

34. Modified cross-validation for penalized high-dimensional linear regression models. (2014) Journal of Computational and Graphical Statistics, 23, 1009--1027. [pdf]
    

    Y. and Yang Feng
    

35. Apple: Approximate Path for Penalized Likelihood Estimators (2013). Statistics and Computing, 24, 803--819 [pdf]
    

    Y. and Yang Feng
    

36. Oracle inequalities for the Lasso in the Cox model (2013). The Annals of Statistics, 41, 1142--1165. [pdf]
    

    Jian Huang, Tingni Sun, Zhiliang Ying, Y. and Cun-Hui Zhang
    

Invited discussions

1. Discussion of “Should we sample a time series more frequently? Decision support via multirate spectrum estimation” by Nason, Powell, Elliott and Smith. (2017) Journal of the Royal Statistical Society, Series A. 180, 384--385[pdf]
   

   Y. and Ivor Cribben
   

2. Invited discussion of Large covariance estimation by thresholding principal orthogonal complements by Fan et al (2013). Journal of Royal Statistical Society, Series B, 75, 656--658. [pdf]
   

   Y. and Richard J. Samworth
   

Software

1. GMPro: Graph Matching with Degree Profiles (2020). Available from cran.
   

2. fcd, an R package for fused community detection (2013). Available from cran.
   

3. APPLE, an R package for Approximate Path for Penalized Likelihood Estimators (2012). Available from cran.
   

Ph.D. thesis

1. Contributions to high-dimensional variable selection. Fudan University, June 2013.
   

   Supervisor: Professor Zhiliang Ying.