Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2007.12911
Cited By
v1
v2
v3 (latest)
Tighter risk certificates for neural networks
Journal of machine learning research (JMLR), 2020
25 July 2020
Maria Perez-Ortiz
Omar Rivasplata
John Shawe-Taylor
Csaba Szepesvári
UQCV
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Tighter risk certificates for neural networks"
50 / 94 papers shown
Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees
Dario Paccagnan
Daniel Marks
M. Campi
S. Garatti
121
3
0
04 Dec 2025
A Framework for Bounding Deterministic Risk with PAC-Bayes: Applications to Majority Votes
Benjamin Leblanc
Pascal Germain
190
1
0
29 Oct 2025
Position: Many generalization measures for deep learning are fragile
Shuofeng Zhang
A. Louis
AAML
366
0
0
21 Oct 2025
PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures
Hind Atbir
Farah Cherfaoui
Guillaume Metzler
Emilie Morvant
Paul Viallard
132
0
0
13 Oct 2025
PAC-Bayesian Reinforcement Learning Trains Generalizable Policies
Abdelkrim Zitouni
Mehdi Hennequin
Juba Agoun
Ryan Horache
Nadia Kabachi
Omar Rivasplata
OffRL
BDL
217
0
0
12 Oct 2025
Some theoretical improvements on the tightness of PAC-Bayes risk certificates for neural networks
Diego García-Pérez
E. Parrado-Hernández
John Shawe-Taylor
191
0
0
09 Oct 2025
RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics
Sai Karthikeya Vemuri
Adithya Ashok Chalain Valapil
Tim Buchner
Joachim Denzler
153
0
0
07 Oct 2025
Reconcile Certified Robustness and Accuracy for DNN-based Smoothed Majority Vote Classifier
Gaojie Jin
Xinping Yi
Xiaowei Huang
AAML
174
1
0
30 Sep 2025
Non-Vacuous Generalization Bounds: Can Rescaling Invariances Help?
Damien Rouchouse
Antoine Gonon
Rémi Gribonval
Benjamin Guedj
167
0
0
30 Sep 2025
Deep Actor-Critics with Tight Risk Certificates
Bahareh Tasdighi
Manuel Haussmann
Yi-Shan Wu
A. Masegosa
M. Kandemir
UQCV
543
0
0
26 May 2025
Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning
Taehoon Kim
Henry Gouk
Minyoung Kim
Timothy M. Hospedales
415
0
0
21 May 2025
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
Romain Chor
Abdellatif Zaidi
Piotr Krasnowski
337
2
0
25 Apr 2025
How good is PAC-Bayes at explaining generalisation?
Antoine Picard-Weibel
Eugenio Clerico
Roman Moscoviz
Benjamin Guedj
351
3
0
11 Mar 2025
Measuring temporal effects of agent knowledge by date-controlled tool use
R. Xian
Qiming Cui
Stefan Bauer
Reza Abbasi-Asl
KELM
394
1
0
06 Mar 2025
Deep Learning is Not So Mysterious or Different
Andrew Gordon Wilson
495
33
0
03 Mar 2025
Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors
International Conference on Learning Representations (ICLR), 2025
Romain Chor
Milad Sefidgaran
Piotr Krasnowski
613
3
0
21 Feb 2025
Model Diffusion for Certifiable Few-shot Transfer Learning
Fady Rezk
Royson Lee
Henry Gouk
Timothy M. Hospedales
Minyoung Kim
501
1
0
10 Feb 2025
PeFLL: Personalized Federated Learning by Learning to Learn
International Conference on Learning Representations (ICLR), 2023
Jonathan Scott
Hossein Zakerinia
Christoph H. Lampert
FedML
767
34
0
17 Jan 2025
Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
Mehdi Hennequin
Abdelkrim Zitouni
K. Benabdeslem
H. Elghazel
Yacine Gaci
404
1
0
09 Nov 2024
Learning via Surrogate PAC-Bayes
Neural Information Processing Systems (NeurIPS), 2024
Antoine Picard-Weibel
Roman Moscoviz
Benjamin Guedj
360
0
0
14 Oct 2024
A Generalization Result for Convergence in Learning-to-Optimize
Michael Sucker
Peter Ochs
478
1
0
10 Oct 2024
Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Mathieu Bazinet
Valentina Zantedeschi
Pascal Germain
MLT
AI4CE
567
3
0
26 Sep 2024
Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts
Anna Mészáros
Szilvia Ujváry
Wieland Brendel
Patrik Reizinger
Ferenc Huszár
321
2
0
09 Sep 2024
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Sanae Lotfi
Yilun Kuang
Brandon Amos
Micah Goldblum
Marc Finzi
Andrew Gordon Wilson
317
19
0
25 Jul 2024
Generalization of Hamiltonian algorithms
Neural Information Processing Systems (NeurIPS), 2024
Andreas Maurer
378
2
0
23 May 2024
Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
Neural Information Processing Systems (NeurIPS), 2024
Yi-Shan Wu
Yijie Zhang
Badr-Eddine Chérief-Abdellatif
Yevgeny Seldin
457
5
0
23 May 2024
Position: Understanding LLMs Requires More Than Statistical Generalization
International Conference on Machine Learning (ICML), 2024
Patrik Reizinger
Szilvia Ujváry
Anna Mészáros
A. Kerekes
Wieland Brendel
Ferenc Huszár
410
24
0
03 May 2024
Better-than-KL PAC-Bayes Bounds
Ilja Kuzborskij
Kwang-Sung Jun
Yulian Wu
Kyoungseok Jang
Francesco Orabona
FedML
462
4
0
14 Feb 2024
More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
Hossein Zakerinia
Amin Behjati
Christoph H. Lampert
FedML
394
11
0
06 Feb 2024
A note on regularised NTK dynamics with an application to PAC-Bayesian training
Eugenio Clerico
Benjamin Guedj
403
2
0
20 Dec 2023
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
Apoorva Sharma
Sushant Veer
Asher Hancock
Heng Yang
Marco Pavone
Anirudha Majumdar
408
12
0
07 Dec 2023
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications
Huayi Tang
Yong Liu
554
3
0
08 Nov 2023
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo
Szilvia Ujváry
Gergely Flamich
Vincent Fortuin
José Miguel Hernández Lobato
253
0
0
30 Oct 2023
Federated Learning with Nonvacuous Generalisation Bounds
Pierre Jobic
Maxime Haddouche
Benjamin Guedj
FedML
299
4
0
17 Oct 2023
Comparing Comparators in Generalization Bounds
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Fredrik Hellström
Benjamin Guedj
333
5
0
16 Oct 2023
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Neural Information Processing Systems (NeurIPS), 2023
S. Mbacke
Florence Clerc
Pascal Germain
DRL
477
18
0
07 Oct 2023
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
International Conference on Machine Learning (ICML), 2023
Dominik Schnaus
Jongseok Lee
Zorah Lähner
Rudolph Triebel
UQCV
BDL
EDL
SSL
UD
273
4
0
15 Jul 2023
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity
Journal of machine learning research (JMLR), 2023
Borja Rodríguez Gálvez
Ragnar Thobaben
Mikael Skoglund
565
16
0
21 Jun 2023
On Certified Generalization in Structured Prediction
Neural Information Processing Systems (NeurIPS), 2023
Bastian Boll
Christoph Schnörr
341
0
0
15 Jun 2023
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
Neural Information Processing Systems (NeurIPS), 2023
Felix Biggs
Antonin Schrab
Arthur Gretton
368
36
0
14 Jun 2023
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
International Conference on Machine Learning (ICML), 2023
Romain Chor
Abdellatif Zaidi
Milad Sefidgaran
Yijun Wan
FedML
326
11
0
09 Jun 2023
Learning via Wasserstein-Based High Probability Generalisation Bounds
Neural Information Processing Systems (NeurIPS), 2023
Paul Viallard
Maxime Haddouche
Umut Simsekli
Benjamin Guedj
422
15
0
07 Jun 2023
Fundamental Tradeoffs in Learning with Prior Information
International Conference on Machine Learning (ICML), 2023
Anirudha Majumdar
290
0
0
26 Apr 2023
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche
Benjamin Guedj
310
4
0
14 Apr 2023
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
International Conference on Machine Learning (ICML), 2023
Micah Goldblum
Marc Finzi
K. Rowan
A. Wilson
UQCV
FedML
694
73
0
11 Apr 2023
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
International Conference on Machine Learning (ICML), 2023
S. Mbacke
Florence Clerc
Pascal Germain
522
12
0
17 Feb 2023
Tighter PAC-Bayes Bounds Through Coin-Betting
Annual Conference Computational Learning Theory (COLT), 2023
Kyoungseok Jang
Kwang-Sung Jun
Ilja Kuzborskij
Francesco Orabona
225
22
0
12 Feb 2023
A unified recipe for deriving (time-uniform) PAC-Bayes bounds
Journal of machine learning research (JMLR), 2023
Ben Chugg
Hongjian Wang
Aaditya Ramdas
790
35
0
07 Feb 2023
Operator theory, kernels, and Feedforward Neural Networks
P. Jorgensen
Myung-Sin Song
James Tian
332
0
0
03 Jan 2023
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
H. Flynn
David Reeb
M. Kandemir
Jan Peters
OffRL
401
10
0
29 Nov 2022
1
2
Next
Page 1 of 2
