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Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization
v1v2v3v4 (latest)

Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization

Annual Conference Computational Learning Theory (COLT), 2021
4 February 2021
Mathieu Even
Laurent Massoulié
ArXiv (abs)PDFHTML

Papers citing "Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization"

15 / 15 papers shown
Meta-learning of shared linear representations beyond well-specified linear regression
Meta-learning of shared linear representations beyond well-specified linear regression
Mathieu Even
Laurent Massoulié
481
0
0
31 Jan 2025
Global convergence of gradient descent for phase retrieval
Global convergence of gradient descent for phase retrieval
Théodore Fougereux
Cédric Josz
Xiaopeng Li
273
0
0
13 Oct 2024
The Relative Gaussian Mechanism and its Application to Private Gradient
  Descent
The Relative Gaussian Mechanism and its Application to Private Gradient DescentInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Aymeric Dieuleveut
Paul Mangold
A. Bellet
432
1
0
29 Aug 2023
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large
  Stepsizes and Edge of Stability
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of StabilityNeural Information Processing Systems (NeurIPS), 2023
Mathieu Even
Scott Pesme
Suriya Gunasekar
Nicolas Flammarion
381
28
0
17 Feb 2023
Dimension-free Bounds for Sums of Independent Matrices and Simple
  Tensors via the Variational Principle
Dimension-free Bounds for Sums of Independent Matrices and Simple Tensors via the Variational Principle
Nikita Zhivotovskiy
361
44
0
18 Aug 2021
A law of robustness for two-layers neural networks
A law of robustness for two-layers neural networks
Sébastien Bubeck
Yuanzhi Li
Dheeraj M. Nagaraj
407
63
0
30 Sep 2020
Statistically Preconditioned Accelerated Gradient Method for Distributed
  Optimization
Statistically Preconditioned Accelerated Gradient Method for Distributed OptimizationInternational Conference on Machine Learning (ICML), 2020
Aymeric Dieuleveut
Lin Xiao
Sébastien Bubeck
Francis R. Bach
Laurent Massoulie
300
66
0
25 Feb 2020
Complexity of Highly Parallel Non-Smooth Convex Optimization
Complexity of Highly Parallel Non-Smooth Convex OptimizationNeural Information Processing Systems (NeurIPS), 2019
Sébastien Bubeck
Qijia Jiang
Y. Lee
Yuanzhi Li
Aaron Sidford
319
61
0
25 Jun 2019
An Introduction to Matrix Concentration Inequalities
An Introduction to Matrix Concentration Inequalities
J. Tropp
863
1,275
0
07 Jan 2015
Estimation in high dimensions: a geometric perspective
Estimation in high dimensions: a geometric perspective
Roman Vershynin
484
137
0
20 May 2014
Communication Efficient Distributed Optimization using an Approximate
  Newton-type Method
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodInternational Conference on Machine Learning (ICML), 2013
Ohad Shamir
Nathan Srebro
Tong Zhang
550
592
0
30 Dec 2013
Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model
Optimal Shrinkage of Eigenvalues in the Spiked Covariance ModelAnnals of Statistics (AoS), 2013
D. Donoho
M. Gavish
Iain M. Johnstone
702
225
0
04 Nov 2013
Comparison-Based Learning with Rank Nets
Comparison-Based Learning with Rank NetsInternational Conference on Machine Learning (ICML), 2012
Amin Karbasi
Stratis Ioannidis
laurent Massoulie
315
27
0
18 Jun 2012
On Some Extensions of Bernstein's Inequality for Self-adjoint Operators
On Some Extensions of Bernstein's Inequality for Self-adjoint Operators
Stanislav Minsker
540
165
0
22 Dec 2011
Randomized Smoothing for Stochastic Optimization
Randomized Smoothing for Stochastic OptimizationSIAM Journal on Optimization (SIOPT), 2011
John C. Duchi
Peter L. Bartlett
Martin J. Wainwright
453
309
0
22 Mar 2011
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