activity
20042023
most citedSimultaneous analysis of Lasso and Dantzig selector

2.5k citations · 4.9k across the 53 of their papers we have counts for

collaborators

68 papers

math.ST2023★ 5 cited

Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm

Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil +1

This work studies minimization problems with zero-order noisy oracle information under the assumption that the objective function is highly smooth and possibly satisfies additional…

math.ST2022

Estimating the minimizer and the minimum value of a regression function under passive design

Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov

We propose a new method for estimating the minimizer and the minimum value of a smooth and strongly convex regression function from the observations co…

math.ST2022

Benign overfitting and adaptive nonparametric regression

Julien Chhor, Suzanne Sigalla, Alexandre B. Tsybakov

In the nonparametric regression setting, we construct an estimator which is a continuous function interpolating the data points with high probability, while attaining minimax optim…

math.ST2022★ 4 cited

A gradient estimator via L1-randomization for online zero-order optimization with two point feedback

Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil +1

This work studies online zero-order optimization of convex and Lipschitz functions. We present a novel gradient estimator based on two function evaluations and randomization on the…

math.ST2021

Variable selection, monotone likelihood ratio and group sparsity

Cristina Butucea, Enno Mammen, Mohamed Ndaoud +1

In the pivotal variable selection problem, we derive the exact non-asymptotic minimax selector over the class of all -sparse vectors, which is also the Bayes selector with respe…

math.ST2021

Assigning Topics to Documents by Successive Projections

Olga Klopp, Maxim Panov, Suzanne Sigalla +1

Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering doc…