Andrea Montanari

21 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0002-0267-8574

Department of Statistics and Department of Mathematics, Stanford University

Publications

• Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition — Journal of the American Statistical Association 2026
• The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime — The Annals of Statistics 2025
• Fundamental limits of low-rank matrix estimation with diverging aspect ratios — The Annals of Statistics 2024
• Dimension free ridge regression — The Annals of Statistics 2024
• Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2024
• The Lasso with general Gaussian designs with applications to hypothesis testing — The Annals of Statistics 2023
• Fundamental barriers to high-dimensional regression with convex penalties — The Annals of Statistics 2022
• Underspecification Presents Challenges for Credibility in Modern Machine Learning — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
• Surprises in high-dimensional ridgeless least squares interpolation — The Annals of Statistics 2022
• The interpolation phase transition in neural networks: Memorization and generalization under lazy training — The Annals of Statistics 2022
• Linearized two-layers neural networks in high dimension — The Annals of Statistics 2021
• Estimation of low-rank matrices via approximate message passing — The Annals of Statistics 2021
• The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning — The Annals of Statistics 2021
• Analysis of a two-layer neural network via displacement convexity — The Annals of Statistics 2020
• Nonnegative Matrix Factorization Via Archetypal Analysis — Journal of the American Statistical Association 2020
• Discussion of: “Nonparametric regression using deep neural networks with ReLU activation function” — The Annals of Statistics 2020
• Generating Random Networks Without Short Cycles — Operations Research 2018
• Online rules for control of false discovery rate and false discovery exceedance — The Annals of Statistics 2018
• Sparse PCA via Covariance Thresholding — Journal of Machine Learning Research 2016 (name-matched, not ORCID-linked)
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression — Journal of Machine Learning Research 2014 (name-matched, not ORCID-linked)
• Matrix Completion from Noisy Entries — Journal of Machine Learning Research 2010 (name-matched, not ORCID-linked)