Pierre C. Bellec
Associate Professor, Department of Statistics, Rutgers University.
I am broadly interested in the properties of machine learning algorithms that extract structured information from noisy/corrupted data, with a focus on providing provable, certifiable guarantees on the output of these algorithms, e.g. with confidence intervals or other forms of uncertainty quantification.
Education
2016: PhD,
ENSAE ParisTech, France, advised by Alexandre Tsybakov.
2012: Part III (MASt), University of Cambridge,
UK.
2011: Diplôme d'Ingénieur, Ecole Polytechnique, France.
All publications
Preprints and submitted articles
 [1]
 Chisquare and normal inference in highdimensional multitask regression.
Pierre C Bellec and Gabriel Romon.
arXiv:2107.07828, 2021.  [1]
 Derivatives and residual distribution of regularized Mestimators with application to adaptive tuning.
Pierre C Bellec and Yiwei Shen.
arXiv:2107.05143, 2021.  [1]
 Asymptotic normality of robust Mestimators with convex penalty.
Pierre C Bellec, Yiwei Shen and CunHui Zhang.
arXiv:2107.03826, 2021.  [1]
 Outofsample error estimate for
robust Mestimators with convex penalty.
Pierre C Bellec.
arXiv:2008.11840, 2020.  [2]

Debiasing convex regularized estimators and
interval estimation in linear models
.
Pierre C Bellec and CunHui Zhang.
arXiv:1912.11943, 2019.  [3]
 The noise barrier and the large
signal bias of the Lasso and other convex estimators.
Pierre C Bellec.
arXiv:1804.01230, 2018.  [5]
 Optimistic lower bounds for
convex regularized leastsquares.
Pierre C Bellec.
arXiv:1703.01332, 2017.  [6]
 Concentration of quadratic forms
under a Bernstein moment assumption.
Pierre C. Bellec.
Technical report. Arxiv:1901.08726, 2014.
Journal articles
 [4]
 Debiasing
the Lasso with degreesoffreedom adjustment.
Pierre C Bellec and CunHui Zhang.
Bernoulli, to appear, 2021.  [1]
 Second order
Stein: SURE for SURE and other applications in highdimensional
inference.
Pierre C Bellec and CunHui Zhang.
Ann. Statist., to appear.  [2]
 Optimal bounds for aggregation of
affine estimators.
Pierre C. Bellec.
Ann. Statist., 46(1):30–59, 02 2018.  [3]
 Sharp oracle inequalities for
Least Squares estimators in shape restricted regression.
Pierre C. Bellec.
Ann. Statist., 46(2):745–780, 2018.  [4]
 Slope meets Lasso: Improved
oracle bounds and optimality.
Pierre C. Bellec, Guillaume Lecué, and
Alexandre B. Tsybakov.
Ann. Statist., 46(6B):3603–3642, 2018.  [5]
 On the prediction loss of the
lasso in the partially labeled setting.
Pierre C. Bellec, Arnak S. Dalalyan,
Edwin Grappin, and Quentin Paris.
Electron. J. Statist., 12(2):3443–3472, 2018.  [6]
 Localized Gaussian width of
Mconvex hulls with applications to Lasso and convex aggregation.
Pierre C Bellec.
Bernoulli, to appear, 2017.  [7]
 Optimal exponential bounds for
aggregation of density estimators.
Pierre C. Bellec.
Bernoulli, 23(1):219–248, 2017.  [8]
 Bounds on the prediction error of
penalized least squares estimators with convex penalty.
Pierre C Bellec and Alexandre B Tsybakov.
In
Modern Problems of Stochastic Analysis and Statistics, Selected Contributions In Honor of Valentin Konakov. Springer, 2017.  [9]
 Towards the study of least
squares estimators with convex penalty.
Pierre C Bellec, Guillaume Lecué, and
Alexandre B Tsybakov.
In
Seminaire et Congres, to appear, number 39. Societe mathematique de France, 2017.  [10]
 A sharp oracle inequality for
GraphSlope.
Pierre C. Bellec, Joseph Salmon, and
Samuel Vaiter.
Electron. J. Statist., 11(2):4851–4870, 2017.  [11]
 Adaptive confidence sets in shape
restricted regression.
Pierre C. Bellec.
Bernoulli, to appear, 2016.  [12]
 Sharp Oracle Bounds for
Monotone and Convex Regression Through Aggregation.
Pierre C. Bellec and Alexandre B. Tsybakov.
Journal of Machine Learning Research, 16:1879–1892, 2015.
Conference proceedings
 [1]

Asymptotic normality and confidence intervals for derivatives of 2layers neural network in the random features model.
Pierre C Bellec and Yiwei Shen.
Accepted in Advances in Neural Information Processing Systems (NeurIPS), 2020, to appear.  [2]
 The costfree nature of optimally
tuning Tikhonov regularizers and other ordered smoothers.
Pierre C Bellec and Dana Yang.
Proceedings of the 37th International Conference on Machine Learning (ICML), pages 1621–1630, 2020.  [3]
 First
order expansion of convex regularized estimators.
Pierre Bellec and Arun Kuchibhotla.
In
Advances in Neural Information Processing Systems (NeurIPS), pages 3457–3468, 2019.  [4]

Aggregation of supports along
the Lasso path.
Pierre C. Bellec.
volume 49 of
Proceedings of Machine Learning Research, pages 488–529, Conference On Learning Theory (COLT), Columbia University, New York, USA, 23–26 Jun 2016. PMLR.
Awards and Grants
 NSF award DMS 1945428 (Principal Investigator): CAREER: PostDifferentiation Inference, 20202024.
 NSF award DMS 1811976 (Principal Investigator): Uncertainty Quantification in HighDimensional Structured Regression Problems, 20182021.
 Blaise Pascal PhD Award, 2017 edition.
Student supervision
 Yiwei Shen (PhD student, 2018present), coadvised with CunHui Zhang.
 Gabriel Romon (MSc, Spring 2020), now PhD at ENSAE.
Courses and teaching material
Some of my teaching material is released under Creative Commons and available at https://github.com/bellecp/CCBYSAteachingmaterial/ .
 654 Stochastic Processes (Spring 2018, 2019).
Some teaching material:
 A takehome assignment on perfect sampling via coupling from the past,
 a homework on the Doobh transform and Markov Chains conditioned on avoiding obstacles,
 a sample midterm exam and another midterm exam.
 680 High dimensional probability (Fall 2018).
 382, 582: Intro to probability theory (Spring 2017, Fall 2018).
 588 Datamining (Fall 2016).
Past and upcoming talks
 Annals of Statistics Editors' invited session at JSM (Joint Statistical Meeting), Seattle, August 2021.
 International Conference on Machine Learning (ICML) 2020 (Virtual)
 Mathematical Methods of Modern Statistics 2, CIRM Luminy, France, June 1519, 2020. (Conference happening virtually due to COVID19).
 OttovonGuerickeUniversität Magdeburg. Fakultät für Mathematik, Germany, January 9, 2020.
 Meeting in Mathematics Statistics, CIRM Luminy, France, December 16, 2019.
 CMStatistics conference, London, UK, December 14, 2019.
 Indiana University, Bloomington, November 18, 2019.
 University of South California, Probability Seminar, Los Angeles, November 2, 2019.
 Statistics Seminar, Columbia University, New York, April 15, 2019.
 Department of Statistics Seminar, University of Michigan, Ann Arbor, October 2018.
 Workshop on HigherOrder Asymptotics and PostSelection Inference (WHOAPSI), Washington University in StLouis, September 2018.
 Joint Statistical Meeting (JSM), Vancouver, August 2018.
 Conference on Statistical Learning and Data Science (SLDS), Columbia University, June 2018.
 Oberwolfach, "Statistical Inference for Structured Highdimensional Models", March 2018.
 'Structural Inference in Statistics' spring school, March 8, 2017. Lubbenau, Spreewald, Germany.
 Meeting in Mathematical Statistics (MMS), Luminy, Dec 2017.
 Baruch College, CUNY, November 2017.
 ICSA, June 2017, Chicago.
 UConn, April 2017. The 31st New England Statistics Symposium.
 NJIT, Department of Mathematical Sciences, April 2017.
 NYU Stern, March 2017.
 MIT, Feb 2017. MIT Stochastics and Statistics seminar series.
 Meeting in Mathematical Statistics (MMS), Dec 2016.
 George Mason University, Nov 18, 2016.
 Conference On Learning Theory (COLT), June 2016. Columbia University,
 Columbia University, Feb 3, 2016. Statistics student seminar.
 Rutgers University, Feb 2, 2016. Statistics seminar.
 Yale University, Jan 29, 2016. YPNG seminar.
 Stanford University, Jan 26, 2016. Statistics seminar.
 Orsay, Laboratoire de Mathematiques, Jan 21, 2016.
 Institut Mathematiques de Toulouse, Jan 12, 2016.
 Meeting in Mathematical Statistics (MMS), Dec 2015.
 Heidelberg University, July 2014. Workshop "Nonparametric and highdimensional statistics".
 Meeting in Mathematical Statistics (MMS), Dec 2014. CIRM, Luminy.
 Yale University, April 2014. YPNG seminar.
Professional Service
 Reviewer for Conference on Learning Theory (COLT) 2016, 2017, 2018, 2019.
 Reviewer for Conference on Neural Information Processing Systems (NeurIPS) 2016, 2017.
 Reviewer for Journal of the Royal Statistical Society, Statistical Methodology, Series B. 2018
 Reviewer for the Annals of Statistics. 20152019
 Reviewer for Bernoulli Journal. 20142018
 Reviewer for the Journal of Multivariate Analysis. 2018
 Reviewer for ESAIM (European Series in Applied and Industrial Mathematics) Probability and Statistics. 2018
 Reviewer for IEEE Transactions on Information Theory. 20172018
 Reviewer for Electronic Journal of Statistics. 20162018
 Reviewer for Scandinavian Journal of Statistics. 2016.
Software
Fastp (https://github.com/bellecp/fastp), a fast commandline tool to browse hundreds or thousands of academic PDFs.
Reach me
Department of Statistics
Rutgers University
501 Hill Center, Busch Campus
110 Frelinghuysen Road
Piscataway, NJ 08854