ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1712.02747
  4. Cited By
Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear
  least squares regression
v1v2 (latest)

Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression

7 December 2017
O. Catoni
Ilaria Giulini
ArXiv (abs)PDFHTML

Papers citing "Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression"

28 / 28 papers shown
Title
Time-Uniform Confidence Spheres for Means of Random Vectors
Time-Uniform Confidence Spheres for Means of Random Vectors
Ben Chugg
Hongjian Wang
Aaditya Ramdas
203
5
0
14 Nov 2023
Covariance Estimation under Missing Observations and $L_4-L_2$ Moment
  Equivalence
Covariance Estimation under Missing Observations and L4−L2L_4-L_2L4​−L2​ Moment Equivalence
Pedro Abdalla
67
1
0
22 May 2023
Dimension-free Bounds for Sum of Dependent Matrices and Operators with
  Heavy-Tailed Distribution
Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution
Shogo H. Nakakita
Pierre Alquier
Masaaki Imaizumi
88
2
0
18 Oct 2022
Covariance Estimation: Optimal Dimension-free Guarantees for Adversarial
  Corruption and Heavy Tails
Covariance Estimation: Optimal Dimension-free Guarantees for Adversarial Corruption and Heavy Tails
Pedro Abdalla
Nikita Zhivotovskiy
84
25
0
17 May 2022
Catoni-style confidence sequences for heavy-tailed mean estimation
Catoni-style confidence sequences for heavy-tailed mean estimation
Hongjian Wang
Aaditya Ramdas
126
32
0
02 Feb 2022
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
79
35
0
18 Aug 2021
High Dimensional Differentially Private Stochastic Optimization with
  Heavy-tailed Data
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
Lijie Hu
Shuo Ni
Hanshen Xiao
Di Wang
145
53
0
23 Jul 2021
Robust learning with anytime-guaranteed feedback
Robust learning with anytime-guaranteed feedback
Matthew J. Holland
OOD
26
0
0
24 May 2021
Concentration study of M-estimators using the influence function
Concentration study of M-estimators using the influence function
Timothée Mathieu
73
8
0
09 Apr 2021
Distribution-Free Robust Linear Regression
Distribution-Free Robust Linear Regression
Jaouad Mourtada
Tomas Vaskevicius
Nikita Zhivotovskiy
OOD
74
24
0
25 Feb 2021
New bounds for $k$-means and information $k$-means
New bounds for kkk-means and information kkk-means
Gautier Appert
O. Catoni
69
5
0
14 Jan 2021
Optimal Mean Estimation without a Variance
Optimal Mean Estimation without a Variance
Yeshwanth Cherapanamjeri
Nilesh Tripuraneni
Peter L. Bartlett
Michael I. Jordan
77
23
0
24 Nov 2020
Differentially Private (Gradient) Expectation Maximization Algorithm
  with Statistical Guarantees
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
Di Wang
Jiahao Ding
Lijie Hu
Zejun Xie
Miao Pan
Jinhui Xu
33
0
0
22 Oct 2020
On Differentially Private Stochastic Convex Optimization with
  Heavy-tailed Data
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang
Hanshen Xiao
S. Devadas
Jinhui Xu
81
57
0
21 Oct 2020
PAC-Bayes unleashed: generalisation bounds with unbounded losses
PAC-Bayes unleashed: generalisation bounds with unbounded losses
Maxime Haddouche
Benjamin Guedj
Omar Rivasplata
John Shawe-Taylor
100
56
0
12 Jun 2020
Universal Robust Regression via Maximum Mean Discrepancy
Universal Robust Regression via Maximum Mean Discrepancy
Pierre Alquier
Mathieu Gerber
139
16
0
01 Jun 2020
Generalized Resilience and Robust Statistics
Generalized Resilience and Robust Statistics
Banghua Zhu
Jiantao Jiao
Jacob Steinhardt
96
45
0
19 Sep 2019
Lecture Notes: Selected topics on robust statistical learning theory
Lecture Notes: Selected topics on robust statistical learning theory
M. Lerasle
OOD
75
32
0
28 Aug 2019
A Unified Approach to Robust Mean Estimation
A Unified Approach to Robust Mean Estimation
Adarsh Prasad
Sivaraman Balakrishnan
Pradeep Ravikumar
63
27
0
01 Jul 2019
Distribution-robust mean estimation via smoothed random perturbations
Distribution-robust mean estimation via smoothed random perturbations
Matthew J. Holland
OOD
65
5
0
25 Jun 2019
Mean estimation and regression under heavy-tailed distributions--a
  survey
Mean estimation and regression under heavy-tailed distributions--a survey
Gabor Lugosi
S. Mendelson
105
246
0
10 Jun 2019
Robust subgaussian estimation of a mean vector in nearly linear time
Robust subgaussian estimation of a mean vector in nearly linear time
Jules Depersin
Guillaume Lecué
143
92
0
07 Jun 2019
PAC-Bayes under potentially heavy tails
PAC-Bayes under potentially heavy tails
Matthew J. Holland
128
42
0
20 May 2019
Robust Inference via Multiplier Bootstrap
Robust Inference via Multiplier Bootstrap
Xi Chen
Wen-Xin Zhou
54
32
0
18 Mar 2019
Fast Mean Estimation with Sub-Gaussian Rates
Fast Mean Estimation with Sub-Gaussian Rates
Yeshwanth Cherapanamjeri
Nicolas Flammarion
Peter L. Bartlett
81
78
0
06 Feb 2019
Robust descent using smoothed multiplicative noise
Robust descent using smoothed multiplicative noise
Matthew J. Holland
OOD
79
27
0
15 Oct 2018
Near-optimal mean estimators with respect to general norms
Near-optimal mean estimators with respect to general norms
Gábor Lugosi
S. Mendelson
61
40
0
16 Jun 2018
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
M. Lerasle
Z. Szabó
Gaspar Massiot
Guillaume Lecué
217
36
0
13 Feb 2018
1