Generalisation error in learning with random features and the hidden manifold model

21 February 2020

Lenka Zdeborová

Papers citing "Generalisation error in learning with random features and the hidden manifold model"

22 / 22 papers shown

Title
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime Francesco Camilli D. Tieplova Eleonora Bergamin Jean Barbier 85 0 0 06 May 2025
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model Kaito Takanami Takashi Takahashi Ayaka Sakata 29 0 0 27 Jan 2025
Asymptotic theory of in-context learning by linear attention Yue M. Lu Mary I. Letey Jacob A. Zavatone-Veth Anindita Maiti C. Pehlevan 19 10 0 20 May 2024
Restoring balance: principled under/oversampling of data for optimal classification Emanuele Loffredo Mauro Pastore Simona Cocco R. Monasson 35 9 0 15 May 2024
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions Nishil Patel Sebastian Lee Stefano Sarao Mannelli Sebastian Goldt Adrew Saxe OffRL 20 3 0 17 Jun 2023
Gibbs-Based Information Criteria and the Over-Parameterized Regime Haobo Chen Yuheng Bu Greg Wornell 21 1 0 08 Jun 2023
Precise Asymptotic Analysis of Deep Random Feature Models David Bosch Ashkan Panahi B. Hassibi 30 19 0 13 Feb 2023
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures Antione Bodin N. Macris 26 4 0 13 Dec 2022
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels Daniel Shwartz Uri Stern D. Weinshall NoLa 25 2 0 02 Oct 2022
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 27 121 0 03 May 2022
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model A. Bodin N. Macris 27 13 0 22 Oct 2021
Learning through atypical "phase transitions" in overparameterized neural networks Carlo Baldassi Clarissa Lauditi Enrico M. Malatesta R. Pacelli Gabriele Perugini R. Zecchina 20 26 0 01 Oct 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 24 71 0 06 Sep 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 15 135 0 16 Feb 2021
Appearance of Random Matrix Theory in Deep Learning Nicholas P. Baskerville Diego Granziol J. Keating 13 11 0 12 Feb 2021
$Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis$ Ising Model Selection Using $\ell_{1}$ -Regularized Linear Regression: A Statistical Mechanics Analysis Xiangming Meng T. Obuchi Y. Kabashima 24 4 0 08 Feb 2021
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 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 33 100 0 25 Jun 2020
When Does Preconditioning Help or Hurt Generalization? S. Amari Jimmy Ba Roger C. Grosse Xuechen Li Atsushi Nitanda Taiji Suzuki Denny Wu Ji Xu 26 32 0 18 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 27 172 0 23 Apr 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 83 152 0 02 Mar 2020
Structured adaptive and random spinners for fast machine learning computations Mariusz Bojarski A. Choromańska K. Choromanski Francois Fagan Cédric Gouy-Pailler Anne Morvan Nourhan Sakr Tamás Sarlós Jamal Atif 25 35 0 19 Oct 2016