Two models of double descent for weak features

18 March 2019

Papers citing "Two models of double descent for weak features"

50 / 262 papers shown

Title
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures Yuan Cao Quanquan Gu M. Belkin 11 51 0 28 Apr 2021
Lower Bounds on the Generalization Error of Nonlinear Learning Models Inbar Seroussi Ofer Zeitouni 13 5 0 26 Mar 2021
The Geometry of Over-parameterized Regression and Adversarial Perturbations J. Rocks Pankaj Mehta AAML 11 8 0 25 Mar 2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou Jingfeng Wu Vladimir Braverman Quanquan Gu Sham Kakade 19 62 0 23 Mar 2021
Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231) Jelena Bradic Yinchu Zhu 17 0 0 21 Mar 2021
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression Yehuda Dar Daniel LeJeune Richard G. Baraniuk MLT 32 5 0 09 Mar 2021
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models Peizhong Ju Xiaojun Lin Ness B. Shroff MLT 24 10 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 16 2 0 08 Mar 2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization Ganesh Ramachandra Kini Orestis Paraskevas Samet Oymak Christos Thrampoulidis 27 93 0 02 Mar 2021
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks Ryumei Nakada Masaaki Imaizumi 21 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 30 135 0 16 Feb 2021
Double-descent curves in neural networks: a new perspective using Gaussian processes Ouns El Harzli Bernardo Cuenca Grau Guillermo Valle Pérez A. Louis 15 6 0 14 Feb 2021
Linear Regression with Distributed Learning: A Generalization Error Perspective Martin Hellkvist Ayça Özçelikkale Anders Ahlén FedML 6 10 0 22 Jan 2021
Adversarially Robust Estimate and Risk Analysis in Linear Regression Yue Xing Ruizhi Zhang Guang Cheng AAML 20 27 0 18 Dec 2020
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks Xiangyu Chang Yingcong Li Samet Oymak Christos Thrampoulidis 32 50 0 16 Dec 2020
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization Kelvin K. Kan J. Nagy Lars Ruthotto AI4CE 37 6 0 11 Dec 2020
Analyzing Finite Neural Networks: Can We Trust Neural Tangent Kernel Theory? Mariia Seleznova Gitta Kutyniok AAML 16 29 0 08 Dec 2020
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately Fereshte Khani Percy Liang FaML 13 65 0 07 Dec 2020
Risk-Monotonicity in Statistical Learning Zakaria Mhammedi 30 7 0 28 Nov 2020
Dimensionality reduction, regularization, and generalization in overparameterized regressions Ningyuan Huang D. Hogg Soledad Villar 21 14 0 23 Nov 2020
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting and Regularization Ke Wang Christos Thrampoulidis 28 27 0 18 Nov 2020
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate Jingfeng Wu Difan Zou Vladimir Braverman Quanquan Gu 8 18 0 04 Nov 2020
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition Ben Adlam Jeffrey Pennington UD 37 93 0 04 Nov 2020
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models J. Rocks Pankaj Mehta 13 41 0 26 Oct 2020
Asymptotic Behavior of Adversarial Training in Binary Classification Hossein Taheri Ramtin Pedarsani Christos Thrampoulidis AAML 21 16 0 26 Oct 2020
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks Eshaan Nichani Adityanarayanan Radhakrishnan Caroline Uhler 21 9 0 19 Oct 2020
A Multi-resolution Theory for Approximating Infinite- $p$ -Zero- $n$ : Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Trade-off Xinran Li X. Meng 10 13 0 17 Oct 2020
What causes the test error? Going beyond bias-variance via ANOVA Licong Lin Edgar Dobriban 19 34 0 11 Oct 2020
Temporal Difference Uncertainties as a Signal for Exploration Sebastian Flennerhag Jane X. Wang Pablo Sprechmann Francesco Visin Alexandre Galashov Steven Kapturowski Diana Borsa N. Heess André Barreto Razvan Pascanu OffRL 8 14 0 05 Oct 2020
On the Universality of the Double Descent Peak in Ridgeless Regression David Holzmüller 8 12 0 05 Oct 2020
Benign overfitting in ridge regression Alexander Tsigler Peter L. Bartlett 28 159 0 29 Sep 2020
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning N. Shoham H. Avron BDL 9 12 0 27 Sep 2020
On the proliferation of support vectors in high dimensions Daniel J. Hsu Vidya Muthukumar Ji Xu 24 42 0 22 Sep 2020
Minimum discrepancy principle strategy for choosing $k$ in $k$ -NN regression Yaroslav Averyanov Alain Celisse 11 0 0 20 Aug 2020
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization Ben Adlam Jeffrey Pennington 15 124 0 15 Aug 2020
On the Generalization Properties of Adversarial Training Yue Xing Qifan Song Guang Cheng AAML 25 32 0 15 Aug 2020
Provable More Data Hurt in High Dimensional Least Squares Estimator Zeng Li Chuanlong Xie Qinwen Wang 15 6 0 14 Aug 2020
The Slow Deterioration of the Generalization Error of the Random Feature Model Chao Ma Lei Wu E. Weinan 20 15 0 13 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 24 440 0 09 Aug 2020
Benign Overfitting and Noisy Features Zhu Li Weijie Su Dino Sejdinovic 10 22 0 06 Aug 2020
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 20 61 0 03 Aug 2020
The Interpolation Phase Transition in Neural Networks: Memorization and Generalization under Lazy Training Andrea Montanari Yiqiao Zhong 47 95 0 25 Jul 2020
Canonical thresholding for non-sparse high-dimensional linear regression I. Silin Jianqing Fan 6 5 0 24 Jul 2020
Early Stopping in Deep Networks: Double Descent and How to Eliminate it Reinhard Heckel Fatih Yilmaz 26 43 0 20 Jul 2020
Prediction in latent factor regression: Adaptive PCR and beyond Xin Bing F. Bunea Seth Strimas-Mackey M. Wegkamp 17 2 0 20 Jul 2020
Large scale analysis of generalization error in learning using margin based classification methods Hanwen Huang Qinglong Yang 9 8 0 16 Jul 2020
Understanding Implicit Regularization in Over-Parameterized Single Index Model Jianqing Fan Zhuoran Yang Mengxin Yu 24 16 0 16 Jul 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip H. S. Torr NoLa AAML 23 57 0 08 Jul 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 25 3 0 19 Jun 2020
On Sparsity in Overparametrised Shallow ReLU Networks Jaume de Dios Joan Bruna 14 14 0 18 Jun 2020