2014. Aggregation of Affine Estimators

With Dong Dai, Lucy Xia and Tong Zhang

Electon. J. Stat., 8, 302-327.

AUTHOR = {Dai, D. and Rigollet, P. and Xia L. and Zhang T.},

TITLE = {Aggregation of Affine Estimators},

Journal = {Electon. J. Stat.},

Pages = {302-327},

Volume = {8},

year = {2014} }

2014. Optimal learning with Q-aggregation

With Guillaume Lecue

Ann. Statist., 42(1), 211-224.

Author = {Lecu\'{e}, Guillaume and Rigollet, Philippe},

Journal = {Ann. Statist.},

Number = {1},

Pages = {211-224},

Title = {Optimal learning with {Q}-aggregation},

Volume = {42},

Year = {2014}

}

2013. Complexity Theoretic Lower Bounds for Sparse Principal Component Detection

With Quentin Berthet

J. Mach. Learn. Res. W&CP, 30, 1046-1066

Best Paper Award.

AUTHOR = {Berthet, Q. and Rigollet, P.},

TITLE = {Complexity Theoretic Lower Bounds for Sparse Principal Component Detection},

JOURNAL = {J. Mach. Learn. Res., W&CP},

FJOURNAL = {Journal of Machine Learning Research (JMLR), W&CP},

YEAR = {2013},

VOLUME = {30},

PAGES = {1046-1066 (electronic)},

}

2013. Bounded regret in stochastic multi-armed bandits

With Sebastien Bubeck and Vianney Perchet

J. Mach. Learn. Res. W&CP, 30, 122-134

Video @ COLT 2013.

AUTHOR = {Bubeck, S. and Perchet, V. and Rigollet, P.},

TITLE = {Bounded regret in stochastic multi-armed bandits},

JOURNAL = {J. Mach. Learn. Res., W&CP},

FJOURNAL = {Journal of Machine Learning Research (JMLR), W&CP},

YEAR = {2013},

VOLUME = {30},

PAGES = {122-134 (electronic)},

}

2013. Optimal detection of sparse principal components in high dimension

With Quentin Berthet

Ann. Statist., 41(1), 1780-1815.

Author = {Berthet, Quentin and Rigollet, Philippe},

Journal = {Ann. Statist.},

Number = {1},

Pages = {1780-1815},

Title = {Optimal detection of sparse principal components in high dimension},

Volume = {41},

Year = {2013}}

2013. The multi-armed bandit problem with covariates

With Vianney Perchet

Ann. Statist., 41(2), 693-721

AUTHOR = {Perchet, V. and Rigollet, P.},

TITLE = {The multi-armed bandit problem with covariates},

YEAR = {2013},

VOLUME = {43},

NUMBER = {2},

Pages = {693-721},

YEAR = {2013},

JOURNAL = {Ann. Statist.},

FJOURNAL = {The Annals of Statistics},

NOTE = {arXiv:1110.6084},

}

2012. Sparse estimation by exponential weighting

With Alexandre Tsybakov

Statist. Sci., 27(4), 558-575

Author = {Rigollet, P. and Tsybakov, A.},

Journal = {Statistical Science},

Number = {4},

Pages = {558-575},

Title = {Sparse estimation by exponential weighting},

Volume = {27},

Year = {2012},

}

2012. Estimation of Covariance Matrices under Sparsity Constraints

With Alexandre Tsybakov

Statist. Sinica, 22(4), 1319-1378.

AUTHOR = {Rigollet, P. and Tsybakov, A.},

TITLE = {Estimation of Covariance Matrices under Sparsity Constraints},

YEAR = {2012},

VOLUME = {22},

NUMBER = {4},

JOURNAL = {Statist. Sinica},

FJOURNAL = {Statistica Sinica},

NOTE = {Discussion of ``Minimax Estimation of Large Covariance Matrices under L1-Norm" by T. Tony Cai and Harrison H. Zhou},

PAGES = {1319-1378}

}

2012. Deviation Optimal Learning using Greedy Q-aggregation

With Dong Dai and Tong Zhang

Ann. Statist., 40(3), 1878-1905

AUTHOR = {Dai, D. and Rigollet, P. and Zhang, T.},

TITLE = {Deviation Optimal Learning using Greedy Q-aggregation},

YEAR = {2012},

MONTH = {March},

JOURNAL = {Ann. Statist.},

FJOURNAL = {The Annals of Statistics},

VOLUME = {40},

NUMBER = {3},

PAGES = {1878-1905},

NOTE = {arXiv:1203.2507},

}

2012. Kullback-Leibler aggregation and misspecified generalized linear models

Ann. Statist., 40(2), 639-665.

Author = {Philippe Rigollet},

Doi = {10.1214/11-AOS961},

Fjournal = {Annals of Statistics},

Issn = {0090-5364},

Journal = {Ann. Statist.},

Number = {2},

Pages = {639-665},

Title = {Kullback--Leibler aggregation and misspecified generalized linear models},

Volume = {40},

Year = {2012},

}

2011. Neyman-Pearson classification, convexity and stochastic constraints

With Xin Tong

J. Mach. Learn. Res., 12(Oct):2831-2855

AUTHOR = {Rigollet, P. and Tong, X.},

TITLE = {Neyman-Pearson classification, convexity and stochastic constraints},

JOURNAL = {J. Mach. Learn. Res.},

FJOURNAL = {Journal of Machine Learning Research (JMLR)},

VOLUME = {12},

YEAR = {2011},

PAGES = {2831-2855 (electronic)},

}

2011. Exponential Screening and optimal rates of sparse
estimation

With Alexandre Tsybakov

Ann. Statist., 39(2), 731-771.

AUTHOR = {Rigollet, P. and Tsybakov, A.},

TITLE = {{E}xponential {S}creening and optimal rates of sparse estimation},

JOURNAL = {Ann. Statist.},

FJOURNAL = {The Annals of Statistics},

VOLUME = {39},

YEAR = {2011},

NUMBER = {2},

PAGES = {731--771},

}

2011. Neyman-Pearson classification under a strict constraint

With Xin Tong

Proceedings of the 24th Annual Conference on Learning Theory
June 9-11, 2011, Budapest, Hungary. J. Mach. Learn. Res., W&CP, 19:595-614.

AUTHOR = {Rigollet, P. and Tong, X.},

TITLE = {Neyman-Pearson classification under a strict constraint},

JOURNAL = {J. Mach. Learn. Res.- W&CP},

FJOURNAL = {Journal of Machine Learning Research (JMLR) - Workshops and Conference Proceedings},

VOLUME = {19},

YEAR = {2011},

PAGES = {595-614 (electronic)},

EE = {http://jmlr.csail.mit.edu/proceedings/papers/v19/rigollet11a.html},

}

2010. Optimal rates of sparse estimation and universal aggregation

With Alexandre Tsybakov

Oberwolfach reports, 7(1), 924-927

In: Modern Nonparametric Statistics: Going Beyond Asymptotic Minimax, Mar.-Apr. 2010

author = {Rigollet, P. and Tsybakov, A.},

title = { Optimal rates of sparse estimation and universal aggregation},

BOOKTITLE = {Modern Nonparametric Statistics: Going Beyond Asymptotic Minimax},

EDITOR = {Birg{\'e}, Lucien and Johanstone, Iain and Spokoiny, Vladimir},

PUBLISHER = {Mathematisches Forschungsinstitut Oberwolfach},

PAGES = {924--927},

SERIES = {Oberwolfach Reports},

VOLUME = {7},

NUMBER = 14},

YEAR = {2010},

}

2010. Nonparametric Bandits with Covariates

With Assaf Zeevi

In COLT (A. T. Kalai and M. Mohri, eds.). Omnipress, 54-66.

*covariate*. The goal is to maximize cumulative expected reward. We derive general lower bounds on the performance of any admissible policy, and develop an algorithm whose performance achieves the order of said lower bound up to logarithmic terms. This is done by decomposing the global problem into suitably ``localized'' bandit problems. Proofs blend ideas from nonparametric statistics and traditional methods used in the bandit literature.

author = {Rigollet, P. and Zeevi, A.},

title = {Nonparametric Bandits with Covariates},

booktitle = {COLT},

year = {2010},

pages = {54-66},

crossref = {colt10},

}

@proceedings{colt10,

editor = {Adam Tauman Kalai and Mehryar Mohri},

title = {23rd Annual Conference on Learning Theory - COLT 2010, Haifa,

Israel, June 27-29, 2010},

booktitle = {COLT},

publisher = {Omnipress},

year = {2010},

}

2009. Optimal rates for plug-in estimators of density level sets

With Régis Vert

Bernoulli, 15(4), 1154-1178.

AUTHOR = {Rigollet, P. and Vert, R.},

TITLE = {Optimal rates for plug-in estimators of density level sets},

JOURNAL = {Bernoulli},

FJOURNAL = {Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability},

VOLUME = {15},

YEAR = {2009},

NUMBER = {4},

PAGES = {1154--1178},

}

2009. Learning by mirror averaging

With Anatoli Juditsky and Alexandre Tsybakov

Ann. Statist., 36(5), 2183-2206.

AUTHOR = {Juditsky, A. and Rigollet, P. and Tsybakov, A. B.},

TITLE = {Learning by mirror averaging},

JOURNAL = {Ann. Statist.},

FJOURNAL = {The Annals of Statistics},

VOLUME = {36},

YEAR = {2008},

NUMBER = {5},

PAGES = {2183--2206},

}

2007. Generalization error bounds in semi-supervised classification under the cluster assumption

J. Mach. Learn. Res., 8(Jul), 1369-1392

AUTHOR = {Rigollet, Philippe},

TITLE = {Generalized error bounds in semi-supervised classification under the cluster assumption},

JOURNAL = {J. Mach. Learn. Res.},

FJOURNAL = {Journal of Machine Learning Research (JMLR)},

VOLUME = {8},

YEAR = {2007},

PAGES = {1369--1392 (electronic)},

}

2007. Linear and convex aggregation of density estimators

With Alexandre Tsybakov

Math. Methods of Statist., 15(3), 260-280

AUTHOR = {Rigollet, Ph. and Tsybakov, A. B.},

TITLE = {Linear and convex aggregation of density estimators},

JOURNAL = {Math. Methods Statist.},

FJOURNAL = {Mathematical Methods of Statistics},

VOLUME = {16},

YEAR = {2007},

NUMBER = {3},

PAGES = {260--280},

}

2006. Adaptive density estimation using the blockwise Stein method

Bernoulli, 12(2), 351-370

AUTHOR = {Rigollet, Philippe},

TITLE = {Adaptive density estimation using the blockwise {S}tein method},

JOURNAL = {Bernoulli},

FJOURNAL = {Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability},

VOLUME = {12},

YEAR = {2006},

NUMBER = {2},

PAGES = {351--370},

}

2005. Mirror averaging, aggregation and model selection

With Anatoli Juditsky and Alexandre Tsybakov

Oberwolfach reports, 2(4), 2688-2691

In: Meeting on Statistical and Probabilistic Methods of Model Selection, October 2005

AUTHOR = {Juditsky, Anatoli and Rigollet, Philippe and Tsybakov, Alexandre},

TITLE = {Mirror averaging, aggregation and model selection},

BOOKTITLE = {Statistische und Probabilistische Methoden der Modellwahl},

EDITOR = {Berger, James and Dette, Holder and Lugosi, Gabor and Munk, Alex},

PUBLISHER = {Mathematisches Forschungsinstitut Oberwolfach},

PAGES = {2688--2691},

SERIES = {Oberwolfach Reports},

VOLUME = {2},

NUMBER = {4},

YEAR = {2005},

}

2005. Oracle inequalities for probability density estimations - French

C. R. Math. Acad. Sci. Paris, 340(1), 59-62

AUTHOR = {Rigollet, Philippe},

TITLE = {In\'egalit\'es d'oracle pour l'estimation d'une densit\'e de probabilit\'e},

JOURNAL = {C. R. Math. Acad. Sci. Paris},

FJOURNAL = {Comptes Rendus Math\'ematique. Acad\'emie des Sciences. Paris},

VOLUME = {340},

YEAR = {2005},

NUMBER = {1},

PAGES = {59--62},

}

2013. Computational Lower Bounds for Sparse PCA

With Quentin Berthet

arXiv:1304.0828

AUTHOR = {Berthet, Q. and Rigollet, P.},

TITLE = {Computational Lower Bounds for Sparse {PCA}},

YEAR = {2013},

MONTH = {April},

NOTE = {arXiv:1304.0828},

}