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High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

10 July 2015

Papers citing "High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification"

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Title
Optimal Implicit Bias in Linear Regression K. N. Varma Babak Hassibi 36 0 0 20 Jun 2025
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias Yuanzhe Hu Kinshuk Goel Vlad Killiakov Yaoqing Yang 61 2 0 06 Jun 2025
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory Arjun Subramonian Elvis Dohmatob 83 0 0 14 Apr 2025
Comparing regularisation paths of (conjugate) gradient estimators in ridge regression Laura Hucker Markus Reiß Thomas Stark 82 1 0 07 Mar 2025
Rethinking Early Stopping: Refine, Then Calibrate Eugene Berta David Holzmüller Michael I. Jordan Francis Bach 143 1 0 31 Jan 2025
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model Kaito Takanami Takashi Takahashi Ayaka Sakata 153 1 0 27 Jan 2025
Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning Pierre C. Bellec Yi Shen 124 13 0 03 Jan 2025
Generalization vs. Specialization under Concept Shift Alex Nguyen David J. Schwab Vudtiwat Ngampruetikorn OOD 66 0 0 23 Sep 2024
Risk and cross validation in ridge regression with correlated samples Alexander B. Atanasov Jacob A. Zavatone-Veth Cengiz Pehlevan 105 5 0 08 Aug 2024
Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view Mihailo Stojnic 54 1 0 13 Jun 2024
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning Michal Dereziñski Christopher Musco Jiaming Yang 123 2 0 09 May 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis Yufan Li Subhabrata Sen Ben Adlam MLT 177 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 97 4 0 29 Mar 2024
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem Simon Segert 90 0 0 18 Nov 2023
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks Behrad Moniri Donghwan Lee Hamed Hassani Yan Sun MLT 104 23 0 11 Oct 2023
Six Lectures on Linearized Neural Networks Theodor Misiakiewicz Andrea Montanari 143 13 0 25 Aug 2023
Generalized equivalences between subsampling and ridge regularization Pratik V. Patil Jin-Hong Du 91 5 0 29 May 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 109 2 0 20 Feb 2023
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures Antione Bodin N. Macris 79 4 0 13 Dec 2022
Asymptotics of the Sketched Pseudoinverse Daniel LeJeune Pratik V. Patil Hamid Javadi Richard G. Baraniuk Robert Tibshirani 54 10 0 07 Nov 2022
On double-descent in uncertainty quantification in overparametrized models Lucas Clarté Bruno Loureiro Florent Krzakala Lenka Zdeborová UQCV 161 14 0 23 Oct 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 83 6 0 14 Oct 2022
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors Nikhil Ghosh M. Belkin 74 7 0 23 Jul 2022
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs Michal Derezinski 81 5 0 21 Jun 2022
Gaussian Universality of Perceptrons with Random Labels Federica Gerace Florent Krzakala Bruno Loureiro Ludovic Stephan Lenka Zdeborová 107 24 0 26 May 2022
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime Hong Hu Yue M. Lu 92 16 0 13 May 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 99 129 0 03 May 2022
Benign Overfitting in Time Series Linear Models with Over-Parameterization Shogo H. Nakakita Masaaki Imaizumi AI4TS 289 5 0 18 Apr 2022
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes Elvis Dohmatob A. Bietti AAML 85 13 0 22 Mar 2022
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize Alexander Wei Wei Hu Jacob Steinhardt 112 72 0 11 Mar 2022
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias Konstantin Donhauser Nicolò Ruggeri Stefan Stojanovic Fanny Yang 94 22 0 07 Mar 2022
Interpolation and Regularization for Causal Learning L. C. Vankadara Luca Rendsburg U. V. Luxburg Debarghya Ghoshdastidar CML 55 1 0 18 Feb 2022
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization Yue Sheng Alnur Ali 122 2 0 20 Jan 2022
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions Mojtaba Sahraee-Ardakan M. Emami Parthe Pandit S. Rangan A. Fletcher 96 9 0 20 Jan 2022
On generalization bounds for deep networks based on loss surface implicit regularization Masaaki Imaizumi Johannes Schmidt-Hieber ODL 83 3 0 12 Jan 2022
Tight bounds for minimum l1-norm interpolation of noisy data Guillaume Wang Konstantin Donhauser Fanny Yang 162 20 0 10 Nov 2021
Data splitting improves statistical performance in overparametrized regimes Nicole Mücke Enrico Reiss Jonas Rungenhagen Markus Klein 58 8 0 21 Oct 2021
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? Dominic Richards Yan Sun Patrick Rebeschini 74 3 0 26 Aug 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui Bruno Loureiro Florent Krzakala Lenka Zdeborová 92 85 0 31 May 2021
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Regularized Least Squares Regression Problems Biqiang Mu Tianshi Chen L. Ljung 34 5 0 21 Apr 2021
How rotational invariance of common kernels prevents generalization in high dimensions Konstantin Donhauser Mingqi Wu Fanny Yang 87 24 0 09 Apr 2021
Lower Bounds on the Generalization Error of Nonlinear Learning Models Inbar Seroussi Ofer Zeitouni 71 5 0 26 Mar 2021
On the interplay between data structure and loss function in classification problems Stéphane dÁscoli Marylou Gabrié Levent Sagun Giulio Biroli 100 17 0 09 Mar 2021
Asymptotics of Ridge Regression in Convolutional Models Mojtaba Sahraee-Ardakan Tung Mai Anup B. Rao Ryan Rossi S. Rangan A. Fletcher MLT 45 2 0 08 Mar 2021
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks Ryumei Nakada Masaaki Imaizumi 57 2 0 28 Feb 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 116 140 0 16 Feb 2021
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens 90 4 0 03 Nov 2020
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers Fan Yang Hongyang R. Zhang Sen Wu Christopher Ré Weijie J. Su 163 11 0 22 Oct 2020
How Important is the Train-Validation Split in Meta-Learning? Yu Bai Minshuo Chen Pan Zhou T. Zhao Jason D. Lee Sham Kakade Haiquan Wang Caiming Xiong 96 53 0 12 Oct 2020
What causes the test error? Going beyond bias-variance via ANOVA Licong Lin Yan Sun 95 34 0 11 Oct 2020