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Surprises in High-Dimensional Ridgeless Least Squares Interpolation

19 March 2019

Papers citing "Surprises in High-Dimensional Ridgeless Least Squares Interpolation"

50 / 69 papers shown

Title
Directional Convergence, Benign Overfitting of Gradient Descent in leaky ReLU two-layer Neural Networks Ichiro Hashimoto MLT 71 0 0 22 May 2025
Training NTK to Generalize with KARE Johannes Schwab Bryan Kelly Semyon Malamud Teng Andrea Xu 125 0 0 16 May 2025
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks Chenyang Zhang Peifeng Gao Difan Zou Yuan Cao OOD MLT 138 0 0 11 Apr 2025
Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification Xiaohan Zhu Nathan Srebro 92 0 0 03 Mar 2025
Improving the statistical efficiency of cross-conformal prediction Matteo Gasparin Aaditya Ramdas 438 0 0 03 Mar 2025
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization Simone Bombari Marco Mondelli 227 0 0 03 Feb 2025
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws M. E. Ildiz Halil Alperen Gozeten Ege Onur Taga Marco Mondelli Samet Oymak 101 5 0 24 Oct 2024
Efficient Optimization Algorithms for Linear Adversarial Training Antônio H. Ribeiro Thomas B. Schon Dave Zahariah Francis Bach AAML 76 2 0 16 Oct 2024
Breaking Neural Network Scaling Laws with Modularity Akhilan Boopathy Sunshine Jiang William Yue Jaedong Hwang Abhiram Iyer Ila Fiete OOD 115 2 0 09 Sep 2024
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning Mohammadamin Banayeeanzade Mahdi Soltanolkotabi Mohammad Rostami CLL LRM 286 4 0 29 Aug 2024
Remove Symmetries to Control Model Expressivity and Improve Optimization Liu Ziyin Yizhou Xu Isaac Chuang AAML 94 4 0 28 Aug 2024
Asymptotic theory of in-context learning by linear attention Yue M. Lu Mary I. Letey Jacob A. Zavatone-Veth Anindita Maiti Cengiz Pehlevan 88 16 0 20 May 2024
Thermodynamic limit in learning period three Yuichiro Terasaki Kohei Nakajima 105 1 0 12 May 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis Yufan Li Subhabrata Sen Ben Adlam MLT 154 1 0 18 Apr 2024
High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile Jérémie Bigot Issa-Mbenard Dabo Camille Male 66 4 0 29 Mar 2024
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data Angus Dempster Geoffrey I. Webb Daniel F. Schmidt 60 0 0 28 Jan 2024
Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models David X. Wu A. Sahai 72 3 0 23 Jun 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 64 2 0 20 Feb 2023
Benign Overfitting in Time Series Linear Models with Over-Parameterization Shogo H. Nakakita Masaaki Imaizumi AI4TS 244 5 0 18 Apr 2022
Deep learning: a statistical viewpoint Peter L. Bartlett Andrea Montanari Alexander Rakhlin 70 278 0 16 Mar 2021
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers Fan Yang Hongyang R. Zhang Sen Wu Christopher Ré Weijie J. Su 117 11 0 22 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 53 125 0 15 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 207 470 0 09 Aug 2020
Prediction in latent factor regression: Adaptive PCR and beyond Xin Bing F. Bunea Seth Strimas-Mackey M. Wegkamp 30 2 0 20 Jul 2020
Asymptotics of Ridge (less) Regression under General Source Condition Dominic Richards Jaouad Mourtada Lorenzo Rosasco 73 73 0 11 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 72 123 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 94 151 0 16 May 2020
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime Niladri S. Chatterji Philip M. Long 54 109 0 25 Apr 2020
Optimal Regularization Can Mitigate Double Descent Preetum Nakkiran Prayaag Venkat Sham Kakade Tengyu Ma 83 133 0 04 Mar 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
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 101 639 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
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 69 243 0 27 Apr 2019
Two models of double descent for weak features M. Belkin Daniel J. Hsu Ji Xu 106 375 0 18 Mar 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 213 1,108 0 18 Feb 2019
Are All Layers Created Equal? Chiyuan Zhang Samy Bengio Y. Singer 70 140 0 06 Feb 2019
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression Ji Xu A. Maleki Kamiar Rahnama Rad Daniel J. Hsu 59 13 0 05 Feb 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
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon Alexander Rakhlin Xiyu Zhai 105 79 0 28 Dec 2018
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 244 1,655 0 28 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 839 0 19 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 198 448 0 21 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 266 1,469 0 09 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 220 1,135 0 09 Nov 2018
The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning Léo Miolane Andrea Montanari 88 93 0 03 Nov 2018
A Continuous-Time View of Early Stopping for Least Squares Alnur Ali J. Zico Kolter Robert Tibshirani 67 97 0 23 Oct 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 64 150 0 22 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 233 1,276 0 04 Oct 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 68 354 0 01 Aug 2018