Andrea Montanari


Robert and Barbara Kleist Professor in the School of Engineering
Professor, Department of Electrical Engineering,
Department of Statistics,
and (by courtesy) Department of Mathematics
Stanford University


Packard 272, Stanford, CA 94304
Tel: (650) 736-7422
Fax: (650) 723-8473

Some representative papers

S. Mei, T. Misiakiewicz, A. Montanari, Learning with invariances in random features and kernel models, 2021

S. Mei, T. Misiakiewicz, A. Montanari, Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration, 2021

A Montanari, Y. Zhong The interpolation phase transition in neural networks: Memorization and generalization under lazy training, 2020

M. Celentano, A. Montanari, Y. Wei, The Lasso with general Gaussian designs with applications to hypothesis testing, 2020

S. Mei, A. Montanari, The generalization error of random features regression: Precise asymptotics and double descent curve, 2019

B. Ghorbani, S. Mei, T. Misiakiewicz, A. Montanari Linearized two-layers neural networks in high dimension, 2019

A. Montanari, Optimization of the Serrington-Kirkpatrick Hamiltonian, 2019

Z. Fan, S. Mei, A. Montanari, TAP free energy, spin glasses, and variational inference, 2018

S. Mei, A. Montanari, P.-M. Nguyen, A Mean Field View of the Landscape of Two-Layers Neural Networks, 2018

A. Montanari, Mean field asymptotics in high-dimensional statistics: From exact results to efficient algorithms, International Congress of Mathematicians, 2018

A. Montanari and N. Sun, Spectral algorithms for tensor completion, 2016

A. Javanmard and A. Montanari, Online Rules for Control of False Discovery Rate and False Discovery Exceedance, 2015

A. Javanmard, A. Montanari, and F. Ricci-Tersenghi, Phase Transitions in Semidefinite Relaxations, (Including Supplementary Information), 2015 (code)

A. Montanari and S.Sen Semidefinite Programs on Sparse Random Graphs and their Application to Community Detection, 2015

D.L. Donoho, A. Maleki, and A. Montanari. Message passing algorithms for compressed sensing, Proc. Natl. Acad. Sci., 2009