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Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes

14 October 2022

Liam Hodgkinson

Christopher van der Heide

Papers citing "Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes"

50 / 58 papers shown

Title
On double-descent in uncertainty quantification in overparametrized models Lucas Clarté Bruno Loureiro Florent Krzakala Lenka Zdeborová UQCV 87 14 0 23 Oct 2022
An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings Yue M. Lu H. Yau 58 26 0 12 May 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 110 58 0 23 Feb 2022
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data Spencer Frei Niladri S. Chatterji Peter L. Bartlett MLT 85 75 0 11 Feb 2022
Theoretical characterization of uncertainty in high-dimensional linear classification Lucas Clarté Bruno Loureiro Florent Krzakala Lenka Zdeborová 65 20 0 07 Feb 2022
Learning curves for Gaussian process regression with power-law priors and targets Hui Jin P. Banerjee Guido Montúfar 62 18 0 23 Oct 2021
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation Ke Wang Vidya Muthukumar Christos Thrampoulidis 62 49 0 21 Jun 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Kevin Roth Gregor Bachmann Sebastian Nowozin Thomas Hofmann CML 57 26 0 11 Jun 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 72 385 0 29 Apr 2021
The Shape of Learning Curves: a Review T. Viering Marco Loog 69 132 0 19 Mar 2021
Hessian Eigenspectra of More Realistic Nonlinear Models Zhenyu Liao Michael W. Mahoney 62 31 0 02 Mar 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 99 140 0 16 Feb 2021
High-Dimensional Gaussian Process Inference with Derivatives Filip de Roos A. Gessner Philipp Hennig GP 33 17 0 15 Feb 2021
Bayesian Neural Network Priors Revisited Vincent Fortuin Adrià Garriga-Alonso Sebastian W. Ober F. Wenzel Gunnar Rätsch Richard Turner Mark van der Wilk Laurence Aitchison BDL UQCV 106 140 0 12 Feb 2021
Sparse sketches with small inversion bias Michal Derezinski Zhenyu Liao Yan Sun Michael W. Mahoney 91 22 0 21 Nov 2020
Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Survey Jiaxin Zhang 54 112 0 02 Nov 2020
Kernel regression in high dimensions: Refined analysis beyond double descent Fanghui Liu Zhenyu Liao Johan A. K. Suykens 52 50 0 06 Oct 2020
On the Universality of the Double Descent Peak in Ridgeless Regression David Holzmüller 71 13 0 05 Oct 2020
Benign overfitting in ridge regression Alexander Tsigler Peter L. Bartlett 75 167 0 29 Sep 2020
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization Ben Adlam Jeffrey Pennington 49 125 0 15 Aug 2020
A statistical theory of cold posteriors in deep neural networks Laurence Aitchison UQCV BDL 54 70 0 13 Aug 2020
Cold Posteriors and Aleatoric Uncertainty Ben Adlam Jasper Snoek Samuel L. Smith BDL UQCV 74 24 0 31 Jul 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 62 205 0 22 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 70 122 0 10 Jun 2020
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent Zhenyu Liao Romain Couillet Michael W. Mahoney 76 92 0 09 Jun 2020
Triple descent and the two kinds of overfitting: Where & why do they appear? Stéphane dÁscoli Levent Sagun Giulio Biroli 44 80 0 05 Jun 2020
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks Z. Fan Zhichao Wang 105 74 0 25 May 2020
A Brief Prehistory of Double Descent Marco Loog T. Viering A. Mey Jesse H. Krijthe David Tax 49 69 0 07 Apr 2020
Optimal Regularization Can Mitigate Double Descent Preetum Nakkiran Prayaag Venkat Sham Kakade Tengyu Ma 81 133 0 04 Mar 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
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 106 653 0 20 Feb 2020
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski Feynman T. Liang Michael W. Mahoney 65 78 0 10 Dec 2019
Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran Gal Kaplun Yamini Bansal Tristan Yang Boaz Barak Ilya Sutskever 121 945 0 04 Dec 2019
A Model of Double Descent for High-dimensional Binary Linear Classification Zeyu Deng A. Kammoun Christos Thrampoulidis 85 148 0 13 Nov 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 95 639 0 14 Aug 2019
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 88 778 0 26 Jun 2019
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves Armin Lederer Jonas Umlauft Sandra Hirche 47 25 0 04 Jun 2019
On the marginal likelihood and cross-validation Edwin Fong Chris Holmes UQCV 102 111 0 21 May 2019
Bayesian Optimization using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab GP 79 70 0 07 May 2019
Harmless interpolation of noisy data in regression Vidya Muthukumar Kailas Vodrahalli Vignesh Subramanian A. Sahai 80 202 0 21 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 194 746 0 19 Mar 2019
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks Charles H. Martin Michael W. Mahoney 44 56 0 24 Jan 2019
Traditional and Heavy-Tailed Self Regularization in Neural Network Models Charles H. Martin Michael W. Mahoney 79 125 0 24 Jan 2019
Scaling description of generalization with number of parameters in deep learning Mario Geiger Arthur Jacot S. Spigler Franck Gabriel Levent Sagun Stéphane dÁscoli Giulio Biroli Clément Hongler Matthieu Wyart 83 196 0 06 Jan 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 240 1,655 0 28 Dec 2018
A jamming transition from under- to over-parametrization affects loss landscape and generalization S. Spigler Mario Geiger Stéphane dÁscoli Levent Sagun Giulio Biroli Matthieu Wyart 61 150 0 22 Oct 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 65 354 0 01 Aug 2018
A Tutorial on Bayesian Optimization P. Frazier GP 111 1,788 0 08 Jul 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 269 3,213 0 20 Jun 2018