Panagiotis Lolas

activity

20162021

most citedRegularization in High-Dimensional Regression and Classification via Random Matrix Theory

10 citations · 12 across the 4 of their papers we have counts for

collaborators

4 papers

math.PR2021

Shrinkage Estimation of Functions of Large Noisy Symmetric Matrices

Panagiotis Lolas, Lexing Ying

We study the problem of estimating functions of a large symmetric matrix $A_{n}$ when we only have access to a noisy estimate $A^_{n} = A_{n} + σ Z_{n} / n .$ We are interested in the…

stat.ME2020★ 1 cited

$σ$ -Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation

Nikolaos Ignatiadis, Panagiotis Lolas

Features in predictive models are not exchangeable, yet common supervised models treat them as such. Here we study ridge regression when the analyst can partition the features into…

math.ST2020★ 10 cited

Regularization in High-Dimensional Regression and Classification via Random Matrix Theory

Panagiotis Lolas

We study general singular value shrinkage estimators in high-dimensional regression and classification, when the number of features and the sample size both grow proportionally to…

math.PR2016★ 1 cited

Interacting particle systems at the edge of multilevel Jack processes

Evgeni Dimitrov, Panagiotis Lolas

We consider a multilevel continuous time Markov chain $X (s; N) = (X_{i}^{j} (s; N) : 1 \leq i \leq j \leq N)$ , which is defined by means of Jack symmetric functions and forms a certain dis…

Shrinkage Estimation of Functions of Large Noisy Symmetric Matrices

σ-Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation

Regularization in High-Dimensional Regression and Classification via Random Matrix Theory

Interacting particle systems at the edge of multilevel Jack processes

$σ$ -Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation