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Department of Statistics

Dr Yi Yu 于怡

 

Yi Yu 于怡

 

 

 

I am an Associate Professor in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously 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).

 

My research is partially funded by DMS-EPSRC: Change Point Detection and Localization in High-Dimensions: Theory and Methods (EP/V013432/1).

 


 

Room 4.11

Department of Statistics, University of Warwick

Coventry, CV4 7AL, United Kingdom.

yi.yu.2@warwick.ac.uk

+44 (0)24761 50134

 

 

I am teaching the High-Dimensional Statistics module in the Academy for PhD Training in Statistics.

 

In Term 2, 2020-21, I hold office hours on Microsoft Teams in the following slots: (1) 8-9am, Wednesdays and (2) 8-9am, Thursdays.

 

 

Editorial Service

 

  1. Associate Editor, Biometrika, 2019-

  2. Editorial board reviewer, Journal of Machine Learning Research, 2020-

 

Publications and preprints

 

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

    Anne Gael Manegueu, Alexandra Carpentier and Y.

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

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

  3. Functional Linear Regression with Mixed Predictors. (2020) arXiv preprint. [pdf]

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

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

    Y.

  5. Localising change points in piecewise polynomials of general degrees. (2020) arXiv preprint. [pdf]

    Y. and Sabyasachi Chatterjee

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

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

  7. Detecting abrupt changes in high-dimensional self-exciting Poisson processes. (2020) arXiv preprint. [pdf]

    Daren Wang, Y. and Rebecca Willett

  8. Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs. (2020) arXiv preprint. [pdf]

    Yaofang Hu, Wanjie Wang and Y.

  9. Change point localization in dependent dynamic nonparametric random dot product graphs. (2019) arXiv preprint. [pdf]

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

  10. Optimal nonparametric multivariate change point detection and localization. (2019) arXiv preprint. [pdf]

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

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

    Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett

 

  1. 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

  2. Localizing changes in high-dimensional regression models. (2021) AISTATS. [pdf]

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

  3. Optimal Covariance Change Point Detection in High Dimension. (2020) Bernoulli, to appear. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  4. 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

  5. Event history analysis of dynamic communication networks. (2020) Biometrika, to appear. [pdf]

    Tony Sit, Zhiliang Ying and Y.

  6. 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

  7. 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.

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

    Y., Jelena Bradic and Richard J. Samworth

  9. Two new approaches for the visualisation of models for network meta-analysis. (2019) BMC Medical Research Methodology, to appear. [pdf]

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

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

    Haeran Cho and Y.

  11. 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.

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

    Ivor Cribben and Y.

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

    Diego Franco Saldana, Y. and Yang Feng

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

    Y., Tengyao Wang and Richard J. Samworth

  15. 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

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

    Y. and Yang Feng

  17. 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.