Implicit Regularization of Random Feature Models

19 February 2020

Papers citing "Implicit Regularization of Random Feature Models"

20 / 20 papers shown

Title
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum Tin Sum Cheng Aurelien Lucchi Anastasis Kratsios David Belius 37 8 0 02 Feb 2024
Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality François Ged M. H. Veiga 25 0 0 22 Mar 2023
Quantitative deterministic equivalent of sample covariance matrices with a general dependence structure Clément Chouard 16 6 0 23 Nov 2022
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics Esha Saha L. Ho Giang Tran 36 5 0 11 Nov 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 30 31 0 30 Sep 2022
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles Luqin Gan Lili Zheng Genevera I. Allen 24 6 0 05 Jun 2022
Regularization-wise double descent: Why it occurs and how to eliminate it Fatih Yilmaz Reinhard Heckel 25 11 0 03 Jun 2022
Generalization Through The Lens Of Leave-One-Out Error Gregor Bachmann Thomas Hofmann Aurelien Lucchi 49 7 0 07 Mar 2022
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning Yuege Xie Bobby Shi Hayden Schaeffer Rachel A. Ward 78 9 0 07 Dec 2021
Multi-scale Feature Learning Dynamics: Insights for Double Descent Mohammad Pezeshki Amartya Mitra Yoshua Bengio Guillaume Lajoie 61 25 0 06 Dec 2021
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model A. Bodin N. Macris 37 13 0 22 Oct 2021
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta M. Wyart 30 31 0 16 Jun 2021
Towards an Understanding of Benign Overfitting in Neural Networks Zhu Li Zhi-Hua Zhou A. Gretton MLT 33 35 0 06 Jun 2021
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition Ben Adlam Jeffrey Pennington UD 37 93 0 04 Nov 2020
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models J. Rocks Pankaj Mehta 13 41 0 26 Oct 2020
Geometric compression of invariant manifolds in neural nets J. Paccolat Leonardo Petrini Mario Geiger Kevin Tyloo M. Wyart MLT 55 34 0 22 Jul 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data Arthur Jacot Berfin cSimcsek Francesco Spadaro Clément Hongler Franck Gabriel 22 67 0 17 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 41 172 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 93 152 0 02 Mar 2020
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 32 214 0 03 Dec 2019