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PAC-Bayes under potentially heavy tails
v1v2 (latest)

PAC-Bayes under potentially heavy tails

20 May 2019
Matthew J. Holland
ArXiv (abs)PDFHTML

Papers citing "PAC-Bayes under potentially heavy tails"

25 / 25 papers shown
Title
Generalization and Robustness of the Tilted Empirical Risk
Generalization and Robustness of the Tilted Empirical Risk
Gholamali Aminian
Amir R. Asadi
Tian Li
Ahmad Beirami
Gesine Reinert
Samuel N. Cohen
71
0
0
28 Sep 2024
A note on generalization bounds for losses with finite moments
A note on generalization bounds for losses with finite moments
Borja Rodríguez Gálvez
Omar Rivasplata
Ragnar Thobaben
Mikael Skoglund
63
0
0
25 Mar 2024
PAC-Bayes-Chernoff bounds for unbounded losses
PAC-Bayes-Chernoff bounds for unbounded losses
Ioar Casado
Luis A. Ortega
A. Masegosa
Aritz Pérez Martínez
113
6
0
02 Jan 2024
Non-Vacuous Generalization Bounds for Large Language Models
Non-Vacuous Generalization Bounds for Large Language Models
Sanae Lotfi
Marc Finzi
Yilun Kuang
Tim G. J. Rudner
Micah Goldblum
Andrew Gordon Wilson
108
25
0
28 Dec 2023
More PAC-Bayes bounds: From bounded losses, to losses with general tail
  behaviors, to anytime validity
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity
Borja Rodríguez Gálvez
Ragnar Thobaben
Mikael Skoglund
154
9
0
21 Jun 2023
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to
  Explain Generalisation
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche
Benjamin Guedj
88
2
0
14 Apr 2023
Mixed moving average field guided learning for spatio-temporal data
Mixed moving average field guided learning for spatio-temporal data
I. Curato
O. Furat
Lorenzo Proietti
Bennet Stroeh
AI4TS
108
2
0
02 Jan 2023
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior:
  From Theory to Practice
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
Jonas Rothfuss
Martin Josifoski
Vincent Fortuin
Andreas Krause
122
10
0
14 Nov 2022
On the Importance of Gradient Norm in PAC-Bayesian Bounds
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Itai Gat
Yossi Adi
Alex Schwing
Tamir Hazan
BDL
97
6
0
12 Oct 2022
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through
  Supermartingales
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales
Maxime Haddouche
Benjamin Guedj
123
22
0
03 Oct 2022
Online PAC-Bayes Learning
Online PAC-Bayes Learning
Maxime Haddouche
Benjamin Guedj
107
22
0
31 May 2022
On PAC-Bayesian reconstruction guarantees for VAEs
On PAC-Bayesian reconstruction guarantees for VAEs
Badr-Eddine Chérief-Abdellatif
Yuyang Shi
Arnaud Doucet
Benjamin Guedj
DRL
109
19
0
23 Feb 2022
On change of measure inequalities for $f$-divergences
On change of measure inequalities for fff-divergences
Antoine Picard-Weibel
Benjamin Guedj
76
13
0
11 Feb 2022
User-friendly introduction to PAC-Bayes bounds
User-friendly introduction to PAC-Bayes bounds
Pierre Alquier
FedML
193
206
0
21 Oct 2021
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
Tianyu Liu
Jie Lu
Zheng Yan
Guangquan Zhang
56
14
0
07 Feb 2021
Upper and Lower Bounds on the Performance of Kernel PCA
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche
Benjamin Guedj
John Shawe-Taylor
120
4
0
18 Dec 2020
Non-exponentially weighted aggregation: regret bounds for unbounded loss
  functions
Non-exponentially weighted aggregation: regret bounds for unbounded loss functions
Pierre Alquier
105
19
0
07 Sep 2020
PAC-Bayes Analysis Beyond the Usual Bounds
PAC-Bayes Analysis Beyond the Usual Bounds
Omar Rivasplata
Ilja Kuzborskij
Csaba Szepesvári
John Shawe-Taylor
123
80
0
23 Jun 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
Learning with CVaR-based feedback under potentially heavy tails
Learning with CVaR-based feedback under potentially heavy tails
Matthew J. Holland
El Mehdi Haress
42
4
0
03 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
Still no free lunches: the price to pay for tighter PAC-Bayes bounds
Still no free lunches: the price to pay for tighter PAC-Bayes bounds
Benjamin Guedj
L. Pujol
FedML
81
23
0
10 Oct 2019
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
Badr-Eddine Chérief-Abdellatif
Pierre Alquier
186
75
0
29 Sep 2019
Efron-Stein PAC-Bayesian Inequalities
Efron-Stein PAC-Bayesian Inequalities
Ilja Kuzborskij
Csaba Szepesvári
86
22
0
04 Sep 2019
Distribution-robust mean estimation via smoothed random perturbations
Distribution-robust mean estimation via smoothed random perturbations
Matthew J. Holland
OOD
57
5
0
25 Jun 2019
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