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Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime

25 April 2020

Niladri S. Chatterji

Papers citing "Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime"

44 / 44 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
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
Provable Weak-to-Strong Generalization via Benign Overfitting David X. Wu A. Sahai 143 10 0 06 Oct 2024
When resampling/reweighting improves feature learning in imbalanced classification?: A toy-model study Tomoyuki Obuchi Toshiyuki Tanaka 109 0 0 09 Sep 2024
Implicit Bias and Fast Convergence Rates for Self-attention Bhavya Vasudeva Puneesh Deora Christos Thrampoulidis 79 21 0 08 Feb 2024
Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models David X. Wu A. Sahai 72 3 0 23 Jun 2023
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent Shahar Azulay E. Moroshko Mor Shpigel Nacson Blake E. Woodworth Nathan Srebro Amir Globerson Daniel Soudry AI4CE 79 74 0 19 Feb 2021
Interpolating Classifiers Make Few Mistakes Tengyuan Liang Benjamin Recht 55 28 0 28 Jan 2021
On the Error Resistance of Hinge Loss Minimization Kunal Talwar 58 5 0 02 Dec 2020
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers Fan Yang Hongyang R. Zhang Sen Wu Christopher Ré Weijie J. Su 133 11 0 22 Oct 2020
On the proliferation of support vectors in high dimensions Daniel J. Hsu Vidya Muthukumar Ji Xu 80 45 0 22 Sep 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 101 151 0 16 May 2020
$A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers$ A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum- $\ell_1$ -Norm Interpolated Classifiers Tengyuan Liang Pragya Sur 108 70 0 05 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 105 779 0 26 Jun 2019
Distribution-Independent PAC Learning of Halfspaces with Massart Noise Ilias Diakonikolas Themis Gouleakis Christos Tzamos 89 83 0 24 Jun 2019
Kernel and Rich Regimes in Overparametrized Models Blake E. Woodworth Suriya Gunasekar Pedro H. P. Savarese E. Moroshko Itay Golan Jason D. Lee Daniel Soudry Nathan Srebro 82 366 0 13 Jun 2019
Harmless interpolation of noisy data in regression Vidya Muthukumar Kailas Vodrahalli Vignesh Subramanian A. Sahai 84 203 0 21 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 216 747 0 19 Mar 2019
Two models of double descent for weak features M. Belkin Daniel J. Hsu Ji Xu 117 375 0 18 Mar 2019
Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan J. Zico Kolter MoMe AI4CE 86 317 0 13 Feb 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 247 1,659 0 28 Dec 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 87 355 0 01 Aug 2018
Does data interpolation contradict statistical optimality? M. Belkin Alexander Rakhlin Alexandre B. Tsybakov 93 221 0 25 Jun 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate M. Belkin Daniel J. Hsu P. Mitra AI4CE 153 259 0 13 Jun 2018
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate Mor Shpigel Nacson Nathan Srebro Daniel Soudry FedML MLT 87 102 0 05 Jun 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro MDE 128 414 0 01 Jun 2018
Classification with imperfect training labels T. Cannings Yingying Fan R. Samworth 54 46 0 29 May 2018
Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization D. Kobak Jonathan Lomond Benoit Sanchez 73 89 0 28 May 2018
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression Emmanuel J. Candes Pragya Sur 81 141 0 25 Apr 2018
A modern maximum-likelihood theory for high-dimensional logistic regression Pragya Sur Emmanuel J. Candes 87 288 0 19 Mar 2018
Characterizing Implicit Bias in Terms of Optimization Geometry Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro AI4CE 73 413 0 22 Feb 2018
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 165 924 0 27 Oct 2017
Implicit Regularization in Deep Learning Behnam Neyshabur 61 147 0 06 Sep 2017
Implicit Regularization in Matrix Factorization Suriya Gunasekar Blake E. Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nathan Srebro 81 493 0 25 May 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 351 4,636 0 10 Nov 2016
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification Yan Sun Stefan Wager 132 289 0 10 Jul 2015
Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen A. Menon Robert C. Williamson NoLa 172 313 0 28 May 2015
Efficient Learning of Linear Separators under Bounded Noise Pranjal Awasthi Maria-Florina Balcan Nika Haghtalab Ruth Urner 63 95 0 12 Mar 2015
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning Behnam Neyshabur Ryota Tomioka Nathan Srebro AI4CE 99 662 0 20 Dec 2014
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising Gilles Blanchard Marek Flaska G. Handy Sara Pozzi Clayton Scott NoLa 111 244 0 05 Mar 2013
On the Necessity of Irrelevant Variables D. Helmbold Philip M. Long CML 81 8 0 12 Mar 2012
A Direct Estimation Approach to Sparse Linear Discriminant Analysis Tony Cai Weidong Liu 97 282 0 18 Jul 2011
Statistical performance of support vector machines Gilles Blanchard Olivier Bousquet P. Massart 392 160 0 03 Apr 2008