Skip to main content Skip to navigation

Department of Statistics

Dr Yi Yu 于怡

 

Yi Yu 于怡

 

 

 

I am a Reader in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously an Associate Professor in the University of Warwick, a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying. I obtained my academic degrees from Fudan University (B.Sc. in Mathematics, June 2009 and Ph.D. in Mathematical Statistics, June 2013).

 

I am an Associate Editor of Biometrika (2019-), an Associate Editor of Bernoulli (2022-) and an editorial board reviewer of Journal of Machine Learning Research (2020-).

 

My research is partially funded by DMS-EPSRC EP/V013432/1 and EPSRC EP/W003716/1.

 


 

Room 4.11

Department of Statistics, University of Warwick

Coventry, CV4 7AL, United Kingdom.

yi.yu.2@warwick.ac.uk

+44 (0)24761 50134

 

 

In 2022-23, I teach Fundamentals of Modern Statistical Inference in Term 1 and High-Dimensional Statistics in the Academy for PhD Training in Statistics in summer.

In Term 1 in 2022-23, my office hours are 8-9am on Thursdays and 12-1pm on Fridays.

 

 

Current members

 

  1. Mengchu Li, PhD student.

  2. Fan Wang, PhD student.

  3. Zilong Xie, visiting PhD student.

  4. Shiqi Liu, visiting PhD student.

 

Past members

 

  1. Haotian Xu, past postdoc.

 

 

Most of my change point analysis papers have methods implemented in the R package changepoints. This package is mainly written by Haotian Xu and will be maintained by the MoChA group.

 

Preprints

 

  1. Change point detection and inference in multivariable nonparametric models under mixing conditions. (2023) arXiv preprint. [pdf]

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

  2. Change point inference in high-dimensional regression models under temporal dependence. (2022) arXiv preprint. [pdf]

    Haotian Xu, Daren Wang, Zifeng Zhao and Y.

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

    Oscar Hernan Madrid Padilla and Y.

  4. On robustness and local differential privacy. (2022) arXiv preprint. [pdf]

    Mengchu Li, Thomas B. Berrett and Y.

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

    Paromita Dubey, Haotian Xu and Y.

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

    Anne Gael Manegueu, Alexandra Carpentier and Y.

  7. Optimal network online change point localisation. (2021) arXiv preprint. [pdf]

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

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

    Y.

  9. A Note on Online Change Point Detection. (2020) arXiv preprint. [pdf]

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

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

    Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett

 

Publications

 

  1. 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.

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

    Mengchu Li, Thomas B. Berrett and Y.

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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

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

    Thomas B. Berrett and Y.

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

    Mengchu Li and Y.

  14. 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

  15. 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.

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

    Daren Wang, Y. and Alessandro Rinaldo

  17. 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

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

    Tony Sit, Zhiliang Ying and Y.

  19. 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

  20. 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.

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

    Y., Jelena Bradic and Richard J. Samworth

  22. 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

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

    Haeran Cho and Y.

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

    Yang Feng and Y.

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

    Ivor Cribben and Y.

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

    Diego Franco Saldana, Y. and Yang Feng

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

    Y., Tengyao Wang and Richard J. Samworth

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

    Y. and Yang Feng

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

    Y. and Yang Feng

  30. 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

 

Notes

 

  1. High-dimensional variable selection in the Cox model. (2010) [pdf]

    Y.

 

Software

 

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

  2. NCPD, an R package for network change point detection using spectral clustering (2015). [source file, manual]

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

  4. 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.