The Interpolating Information Criterion for Overparameterized Models

15 July 2023

Liam Hodgkinson

Christopher van der Heide

Papers citing "The Interpolating Information Criterion for Overparameterized Models"

39 / 39 papers shown

Title
Riemannian Laplace approximations for Bayesian neural networks Federico Bergamin Pablo Moreno-Muñoz Søren Hauberg Georgios Arvanitidis BDL 61 7 0 12 Jun 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 47 5 0 14 Oct 2022
SAM as an Optimal Relaxation of Bayes Thomas Möllenhoff Mohammad Emtiyaz Khan BDL 57 34 0 04 Oct 2022
On the Maximum Hessian Eigenvalue and Generalization Simran Kaur Jérémy E. Cohen Zachary Chase Lipton 50 42 0 21 Jun 2022
Fast Finite Width Neural Tangent Kernel Roman Novak Jascha Narain Sohl-Dickstein S. Schoenholz AAML 49 54 0 17 Jun 2022
On quantitative Laplace-type convergence results for some exponential probability measures, with two applications Asish Bera M. Nasipuri 71 5 0 25 Oct 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 80 76 0 09 Jul 2021
Hessian Eigenspectra of More Realistic Nonlinear Models Zhenyu Liao Michael W. Mahoney 51 29 0 02 Mar 2021
On the Origin of Implicit Regularization in Stochastic Gradient Descent Samuel L. Smith Benoit Dherin David Barrett Soham De MLT 32 204 0 28 Jan 2021
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort Gintare Karolina Dziugaite Mansheej Paul Sepideh Kharaghani Daniel M. Roy Surya Ganguli 93 189 0 28 Oct 2020
Deep Learning is Singular, and That's Good Daniel Murfet Susan Wei Biwei Huang Hui Li Jesse Gell-Redman T. Quella UQCV 53 28 0 22 Oct 2020
Benign overfitting in ridge regression Alexander Tsigler Peter L. Bartlett 58 166 0 29 Sep 2020
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks Like Hui M. Belkin UQCV AAML VLM 46 171 0 12 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 64 89 0 09 Jun 2020
Triple descent and the two kinds of overfitting: Where & why do they appear? Stéphane dÁscoli Levent Sagun Giulio Biroli 39 80 0 05 Jun 2020
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data Charles H. Martin Tongsu Peng Peng Michael W. Mahoney 78 108 0 17 Feb 2020
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective Kaixuan Huang Yuqing Wang Molei Tao T. Zhao MLT 51 97 0 14 Feb 2020
PyHessian: Neural Networks Through the Lens of the Hessian Z. Yao A. Gholami Kurt Keutzer Michael W. Mahoney ODL 45 302 0 16 Dec 2019
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski Feynman T. Liang Michael W. Mahoney 55 78 0 10 Dec 2019
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 64 777 0 26 Jun 2019
On the marginal likelihood and cross-validation Edwin Fong Chris Holmes UQCV 92 110 0 21 May 2019
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks Gauthier Gidel Francis R. Bach Simon Lacoste-Julien AI4CE 63 155 0 30 Apr 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 153 743 0 19 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 193 1,638 0 28 Dec 2018
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning Charles H. Martin Michael W. Mahoney AI4CE 92 201 0 02 Oct 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 224 3,191 0 20 Jun 2018
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning Yao Zhang Andrew M. Saxe Madhu S. Advani A. Lee 52 60 0 05 Mar 2018
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries Z. Yao A. Gholami Qi Lei Kurt Keutzer Michael W. Mahoney 61 167 0 22 Feb 2018
High-dimensional dynamics of generalization error in neural networks Madhu S. Advani Andrew M. Saxe AI4CE 128 469 0 10 Oct 2017
Implicit Regularization in Deep Learning Behnam Neyshabur 50 146 0 06 Sep 2017
Implicit Regularization in Matrix Factorization Suriya Gunasekar Blake E. Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nathan Srebro 75 491 0 25 May 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 52 598 0 13 Apr 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 390 2,934 0 15 Sep 2016
PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain Francis R. Bach Alexandre Lacoste Simon Lacoste-Julien 62 183 0 27 May 2016
Fluctuation Analysis of Adaptive Multilevel Splitting Frédéric Cérou A. Guyader 60 31 0 27 Aug 2014
A General Framework for Updating Belief Distributions Pier Giovanni Bissiri Chris Holmes S. Walker 172 476 0 27 Jun 2013
A Widely Applicable Bayesian Information Criterion Sumio Watanabe 83 781 0 31 Aug 2012
Sampling From A Manifold P. Diaconis Susan P. Holmes M. Shahshahani 82 116 0 28 Jun 2012
Implementing regularization implicitly via approximate eigenvector computation Michael W. Mahoney L. Orecchia 94 44 0 04 Oct 2010