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Paul Viallard
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2020 – today
- 2025
- [c7]Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj:
A PAC-Bayesian Link Between Generalisation and Flat Minima. ALT 2025: 481-511 - 2024
- [j1]Paul Viallard
, Pascal Germain, Amaury Habrard, Emilie Morvant:
A general framework for the practical disintegration of PAC-Bayesian bounds. Mach. Learn. 113(2): 519-604 (2024) - [c6]Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. AISTATS 2024: 3007-3015 - [c5]Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu:
A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint. ECML/PKDD (4) 2024: 283-300 - [i10]Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj:
Tighter Generalisation Bounds via Interpolation. CoRR abs/2402.05101 (2024) - [i9]Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj:
A PAC-Bayesian Link Between Generalisation and Flat Minima. CoRR abs/2402.08508 (2024) - [i8]Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. CoRR abs/2402.13285 (2024) - [i7]Benjamin Dupuis, Paul Viallard, George Deligiannidis
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets. CoRR abs/2404.17442 (2024) - 2023
- [c4]Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj:
Learning via Wasserstein-Based High Probability Generalisation Bounds. NeurIPS 2023 - [i6]Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj:
Learning via Wasserstein-Based High Probability Generalisation Bounds. CoRR abs/2306.04375 (2023) - [i5]Benjamin Dupuis, Paul Viallard:
From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD. CoRR abs/2312.00427 (2023) - 2022
- [b1]Paul Viallard:
PAC-Bayesian Bounds and Beyond: Self-Bounding Algorithms and New Perspectives on Generalization in Machine Learning. (Bornes PAC-Bayésiennes et Au-delà: Algorithmes Auto-limitatifs et Nouvelles Perspectives sur la Généralisation en Apprentissage Automatique). Jean Monnet University, Saint-Étienne, France, 2022 - 2021
- [c3]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. NeurIPS 2021: 455-467 - [c2]Paul Viallard, Guillaume Vidot, Amaury Habrard, Emilie Morvant:
A PAC-Bayes Analysis of Adversarial Robustness. NeurIPS 2021: 14421-14433 - [c1]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. ECML/PKDD (2) 2021: 167-183 - [i4]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
A General Framework for the Derandomization of PAC-Bayesian Bounds. CoRR abs/2102.08649 (2021) - [i3]Guillaume Vidot
A PAC-Bayes Analysis of Adversarial Robustness. CoRR abs/2102.11069 (2021) - [i2]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. CoRR abs/2104.13626 (2021) - [i1]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. CoRR abs/2106.12535 (2021)
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