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Iterative Regularization for Learning with Convex Loss Functions
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Iterative Regularization for Learning with Convex Loss Functions

Junhong Lin
Lorenzo Rosasco
Ding-Xuan Zhou
ArXiv (abs)PDFHTML

Papers citing "Iterative Regularization for Learning with Convex Loss Functions"

19 / 19 papers shown
Title
Towards Weaker Variance Assumptions for Stochastic Optimization
Towards Weaker Variance Assumptions for Stochastic Optimization
Ahmet Alacaoglu
Yura Malitsky
Stephen J. Wright
84
1
0
14 Apr 2025
Lp- and Risk Consistency of Localized SVMs
Lp- and Risk Consistency of Localized SVMs
Hannes Köhler
119
0
0
16 May 2023
Random Smoothing Regularization in Kernel Gradient Descent Learning
Random Smoothing Regularization in Kernel Gradient Descent Learning
Liang Ding
Tianyang Hu
Jiahan Jiang
Donghao Li
Wei Cao
Yuan Yao
74
6
0
05 May 2023
Iterative regularization in classification via hinge loss diagonal descent
Iterative regularization in classification via hinge loss diagonal descent
Vassilis Apidopoulos
T. Poggio
Lorenzo Rosasco
S. Villa
65
2
0
24 Dec 2022
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition
Varun Kanade
Patrick Rebeschini
Tomas Vaskevicius
78
10
0
23 Feb 2022
From inexact optimization to learning via gradient concentration
From inexact optimization to learning via gradient concentration
Bernhard Stankewitz
Nicole Mücke
Lorenzo Rosasco
88
5
0
09 Jun 2021
Learning with Gradient Descent and Weakly Convex Losses
Learning with Gradient Descent and Weakly Convex Losses
Dominic Richards
Michael G. Rabbat
MLT
71
15
0
13 Jan 2021
The Statistical Complexity of Early-Stopped Mirror Descent
The Statistical Complexity of Early-Stopped Mirror Descent
Tomas Vaskevicius
Varun Kanade
Patrick Rebeschini
90
23
0
01 Feb 2020
Large-scale Kernel Methods and Applications to Lifelong Robot Learning
Large-scale Kernel Methods and Applications to Lifelong Robot Learning
Raffaello Camoriano
84
1
0
11 Dec 2019
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
Chen Wang
Qin Yu
Kai Zhou
D. Hui
Xiaofeng Gong
Ruisen Luo
141
22
0
09 Aug 2019
Improved Classification Rates for Localized SVMs
Improved Classification Rates for Localized SVMs
Ingrid Blaschzyk
Ingo Steinwart
47
5
0
04 May 2019
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Dominic Richards
Patrick Rebeschini
73
18
0
18 Sep 2018
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Erwan Fouillen
C. Boyer
Maxime Sangnier
FedML
77
2
0
29 Aug 2018
Efficacy of regularized multi-task learning based on SVM models
Efficacy of regularized multi-task learning based on SVM models
Shaohan Chen
Zhou Fang
Sijie Lu
Chuanhou Gao
25
9
0
31 May 2018
Optimal Rates for Learning with Nyström Stochastic Gradient Methods
Optimal Rates for Learning with Nyström Stochastic Gradient Methods
Junhong Lin
Lorenzo Rosasco
98
7
0
21 Oct 2017
Spatial Decompositions for Large Scale SVMs
Spatial Decompositions for Large Scale SVMs
P. Thomann
Ingrid Blaschzyk
Mona Meister
Ingo Steinwart
56
21
0
01 Dec 2016
Distributed learning with regularized least squares
Distributed learning with regularized least squares
Shaobo Lin
Xin Guo
Ding-Xuan Zhou
186
191
0
11 Aug 2016
Alternative asymptotics for cointegration tests in large VARs
Alternative asymptotics for cointegration tests in large VARs
Junhong Lin
Lorenzo Rosasco
72
37
0
28 May 2016
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Junhong Lin
Raffaello Camoriano
Lorenzo Rosasco
81
70
0
26 May 2016