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 2021-22, 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 2021-22 Term 2, my office hours are 12-1pm on Wednesdays and 12-1pm on Thursdays. Please send me an email in advance to book a slot.

 

My current Ph.D. students are Mengchu Li (2019-) and Fan Wang (2020-). My current postdoc is Haotian Xu (2021-).

 

 

Most of my change point analysis papers have methods implemented in the R package changepoints. This package will be maintained by my group.

 

Editorial Service

 

1. Associate Editor, Bernoulli, 2022-

2. Associate Editor, Biometrika, 2019-

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

 

Preprints

 

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

   Mengchu Li, Thomas B. Berrett and Y.
   

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

   Paromita Dubey, Haotian Xu and Y.
   

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

   Anne Gael Manegueu, Alexandra Carpentier and Y.
   

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

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

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

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

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

   Y.
   

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

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

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

   Daren Wang, Y. and Rebecca Willett
   

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

    Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett
    

 

Publications

 

1. Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. (2022) AISTATS (oral). [pdf]
   

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

2. Optimal partition recovery in general graphs. (2022) AISTATS. [pdf]
   

   Y., Oscar Hernan Madrid Padilla and Alessandro Rinaldo
   

3. Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs. (2021) Statistics and Computing, to appear. [pdf]
   

   Yaofang Hu, Wanjie Wang and Y.
   

4. Localising change points in piecewise polynomials of general degrees. (2021) Electronic Journal of Statistics, to appear. [pdf]
   

   Y., Sabyasachi Chatterjee and Haotian Xu
   

5. Optimal nonparametric multivariate change point detection and localization. (2021) IEEE Transactions on Information Theory. [pdf]
   

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

6. Lattice partition recovery with dyadic CART. (2021) NeurIPS. [pdf]
   

   Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo
   

7. Locally private online change point detection. (2021) NeurIPS. [pdf]
   

   Thomas B. Berrett and Y.
   

8. Adversarially Robust Change Point Detection. (2021) NeurIPS. [pdf]
   

   Mengchu Li and Y.
   

9. 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
   

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

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

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

    Daren Wang, Y. and Alessandro Rinaldo
    

12. 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
    

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

    Tony Sit, Zhiliang Ying and Y.
    

14. 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
    

15. 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.
    

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

    Y., Jelena Bradic and Richard J. Samworth
    

17. 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
    

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

    Haeran Cho and Y.
    

19. 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.
    

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

    Ivor Cribben and Y.
    

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

    Diego Franco Saldana, Y. and Yang Feng

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

    Y., Tengyao Wang and Richard J. Samworth
    

23. 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
    

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

    Y. and Yang Feng
    

25. 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.