Research

Preprints

1. 7. Differentially private hypothesis testing in survival analysis. (2026) arXiv preprint. [pdf]
   

   Elly K. H. Hung and Y.
   

2. 6. Online learning for autoregressive multilayer stochastic block models under stationarity and non-stationarity. (2026) arXiv preprint. [pdf]
   

   Fan Wang, Haotian Xu and Y.
   

3. 5. Federated fairness-aware classification under differential privacys. (2026) arXiv preprint. [pdf]
   

   Gengyu Xue and Y.
   

4. 4. Optimal Cox regression under federated differential privacy: coefficients and cumulative hazards. (2025) arXiv preprint. [pdf]
   

   Elly K. H. Hung and Y.
   

5. 3. Locally Private Nonparametric Contextual Multi-armed Bandits. (2025) arXiv preprint. [pdf]
   

   Yuheng Ma, Feiyu Jiang, Zifeng Zhao, Hanfang Yang and Y.
   

6. 2. Optimal estimation in private distributed functional data analysis. (2024) arXiv preprint. [pdf]
   

   Gengyu Xue, Zhenhua Lin and Y.
   

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

   Mengchu Li, Ye Tian, Yang Feng and Y.
   

Publications

1. 48. Locally differentially private two-sample testing. (2026) Biometrika, to appear. [pdf]
   

   Alexander Kent, Thomas B. Berrett and Y.
   

2. 47. Rate Optimality and Phase Transition for User-Level Local Differential Privacy. (2026) Journal of the American Statistical Association, to appear. [pdf]
   

   Alexander Kent, Thomas B. Berrett and Y.
   

3. 46. Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints. (2026) Journal of the American Statistical Association, to appear. [pdf]
   

   Zifeng Zhao, Feiyu Jiang and Y.
   

4. 45. High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection. (2025) Management Science, https://doi.org/10.1287/mnsc.2023.00889. [pdf]
   

   Zifeng Zhao, Feiyu Jiang, Y. and Xi Chen
   

5. 44. Robust mean change point testing in high-dimensional data with heavy tails. (2025) IEEE Transactions on Information Theory, 72.1 (2025): 571-609. [pdf]
   

   Mengchu Li, Yudong Chen, Tengyao Wang and Y.
   

6. 43. Fairness-aware Bayes optimal functional classification. (2026) Advances in Neural Information Processing Systems, 38, 65004-65080. [pdf]
   

   Xiaoyu Hu, Gengyu Xue, Zhenhua Lin and Y.
   

7. 42. Online network change point detection with missing values. (2026) Journal of Time Series Analysis, 47(3), 687-700. [pdf]
   

   Paromita Dubey, Haotian Xu and Y.
   

8. 41. Change point localisation and inference in fragmented functional data. (2025) Bernoulli, Vol. 32, No. 2, 1456-1480. [pdf]
   

   Gengyu Xue, Haotian Xu and Y.
   

9. 40. Multilayer random dot product graphs: Estimation and online change point detection. (2026) Journal of the Royal Statistical Society Series B, 88(1), 282-312. [pdf]
   

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

10. 39. Transfer learning for nonparametric contextual dynamic pricing. (2025) In International Conference on Machine Learning, pp. 63211-63244. PMLR. [pdf]
    

    Fan Wang, Feiyu Jiang, Zifeng Zhao and Y.
    

11. 38. Challenges and Opportunities for Statistics in the Era of Data Science. (2025) Harvard Data Science Review, 7(2). [pdf]
    

    Claudia Kirch, ..., Y. and Johannes Lederer
    

12. 37. Transfer learning for piecewise-constant mean estimation: Optimality, ell_1- and ell_0-penalisation. (2025) Biometrika, 112(3), asaf018. [pdf]
    

    Fan Wang and Y.
    

13. 36. Quickest Detection in High-Dimensional Linear Regression Models via Implicit Regularization. (2024) In 2024 IEEE International Symposium on Information Theory (ISIT) (pp. 1059-1064). IEEE.
    

    Qunzhi Xu, Y. and Yajun Mei
    

14. 35. Change point inference in high-dimensional regression models under temporal dependence. (2024) Annals of Statistics, 52(3), 999-1026. [pdf]
    

    Haotian Xu, Daren Wang, Zifeng Zhao and Y.
    

15. 34. Network online change point localisation. (2023) SIAM Journal on Mathematics of Data Science, 6(1), 176-198. [pdf]
    

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

16. 33. A Note on Online Change Point Detection. (2023) Sequential Analysis, 42(4), 438-471. [pdf]
    

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

17. 32. Change point detection and inference in multivariable nonparametric models under mixing conditions. (2023) Advances in Neural Information Processing Systems, 36, 21081-21134. [pdf]
    

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

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

    Mengchu Li, Thomas B. Berrett and Y.
    

19. 30. Change-point Detection for Sparse and Dense Functional Data in General Dimensions. (2022) Advances in Neural Information Processing Systems, 35, 37121-37133. [pdf]
    

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

20. 29. Network change point localisation under local differential privacy. (2022) Advances in Neural Information Processing Systems, 35, 15013-15026. [pdf]
    

    Mengchu Li, Thomas B. Berrett and Y.
    

21. 28. 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
    

22. 27. 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
    

23. 26. Detecting abrupt changes in high-dimensional self-exciting Poisson processes. (2022) Statistica Sinica, 33, 1653-1671. [pdf]
    

    Daren Wang, Y. and Rebecca Willett
    

24. 25. 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
    

25. 24. 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
    

26. 23. 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.
    

27. 22. 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
    

28. 21. 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
    

29. 20. 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
    

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

    Thomas B. Berrett and Y.
    

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

    Mengchu Li and Y.
    

32. 17. 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
    

33. 16. 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.
    

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

    Daren Wang, Y. and Alessandro Rinaldo
    

35. 14. 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
    

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

    Tony Sit, Zhiliang Ying and Y.
    

37. 12. 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
    

38. 11. 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.
    

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

    Y., Jelena Bradic and Richard J. Samworth
    

40. 9. 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
    

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

    Haeran Cho and Y.
    

42. 7. 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.
    

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

    Ivor Cribben and Y.
    

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

    Diego Franco Saldana, Y. and Yang Feng

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

    Y., Tengyao Wang and Richard J. Samworth
    

46. 3. 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
    

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

    Y. and Yang Feng
    

48. 1. 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. 3. Discussion of “Root and community inference on the latent growth process of a network” by Crane and Xu. (2024) Journal of the Royal Statistical Society, Series B, (86)4, 883--884. [pdf]
   

   Fan Wang, Alessandro Rinaldo and Y.

2. 2. 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 C, 180, 384--385[pdf]
   

   Y. and Ivor Cribben
   

3. 1. 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. 3. Most of my change point analysis papers have methods implemented in the R package changepoints. This package is mainly written by Haotian Xu.

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

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

4. 1. 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.
   

Notes

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

   Oscar Hernan Madrid Padilla and Y.
   

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

   Anne Gael Manegueu, Alexandra Carpentier and Y.
   

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

   Y.
   

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

   Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett