Are Gaussian data all you need? Extents and limits of universality in
high-dimensional generalized linear estimation

Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation

17 February 2023

Florent Krzakala

Ludovic Stephan

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Papers citing "Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation"

19 / 19 papers shown

Title
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model Kaito Takanami Takashi Takahashi Ayaka Sakata 95 1 0 27 Jan 2025
Restoring balance: principled under/oversampling of data for optimal classification Emanuele Loffredo Mauro Pastore Simona Cocco R. Monasson 75 9 0 15 May 2024
Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models David X. Wu A. Sahai 67 3 0 23 Jun 2023
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation Jimmy Ba Murat A. Erdogdu Taiji Suzuki Zhichao Wang Denny Wu Greg Yang MLT 89 129 0 03 May 2022
Universality of empirical risk minimization Andrea Montanari Basil Saeed OOD 63 78 0 17 Feb 2022
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data Umberto M. Tomasini Antonio Sclocchi Matthieu Wyart 66 12 0 07 Feb 2022
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration Song Mei Theodor Misiakiewicz Andrea Montanari 84 112 0 26 Jan 2021
The Lasso with general Gaussian designs with applications to hypothesis testing Michael Celentano Andrea Montanari Yuting Wei 80 64 0 27 Jul 2020
The Gaussian equivalence of generative models for learning with shallow neural networks Sebastian Goldt Bruno Loureiro Galen Reeves Florent Krzakala M. Mézard Lenka Zdeborová BDL 85 107 0 25 Jun 2020
$On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression$ On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression Denny Wu Ji Xu 70 123 0 10 Jun 2020
Classification vs regression in overparameterized regimes: Does the loss function matter? Vidya Muthukumar Adhyyan Narang Vignesh Subramanian M. Belkin Daniel J. Hsu A. Sahai 92 151 0 16 May 2020
The role of regularization in classification of high-dimensional noisy Gaussian mixture Francesca Mignacco Florent Krzakala Yue M. Lu Lenka Zdeborová 40 90 0 26 Feb 2020
Generalisation error in learning with random features and the hidden manifold model Federica Gerace Bruno Loureiro Florent Krzakala M. Mézard Lenka Zdeborová 67 172 0 21 Feb 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 229 208 0 07 Feb 2020
A Model of Double Descent for High-dimensional Binary Linear Classification Zeyu Deng A. Kammoun Christos Thrampoulidis 85 148 0 13 Nov 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 194 746 0 19 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 242 1,655 0 28 Dec 2018
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression Emmanuel J. Candes Pragya Sur 66 141 0 25 Apr 2018
Observed Universality of Phase Transitions in High-Dimensional Geometry, with Implications for Modern Data Analysis and Signal Processing D. Donoho Jared Tanner 94 465 0 14 Jun 2009

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