Paul Viallard

According to our database, Paul Viallard authored at least 16 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers.

Julien Bastian, Benjamin Leblanc, Pascal Germain, Amaury Habrard, Christine Largeron, Guillaume Metzler, Emilie Morvant, Paul Viallard

CoRR, February, 2026

MiniAMIE: Quick and Dirty Rule Mining on Knowledge Graphs.

Luis Galárraga, Julianne Guerbette, Isseïnie Sinouvassane, Paul Viallard

Proceedings of the ACM Web Conference 2026, 2026

2025

PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures.

Hind Atbir, Farah Cherfaoui, Guillaume Metzler, Emilie Morvant, Paul Viallard

CoRR, October, 2025

A PAC-Bayesian Link Between Generalisation and Flat Minima.

Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj

Proceedings of the International Conference on Algorithmic Learning Theory, 2025

2024

A general framework for the practical disintegration of PAC-Bayesian bounds.

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

Mach. Learn., February, 2024

Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets.

Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli

J. Mach. Learn. Res., 2024

Tighter Generalisation Bounds via Interpolation.

Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj

CoRR, 2024

A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint.

Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures.

Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD.

Benjamin Dupuis, Paul Viallard

CoRR, 2023

Learning via Wasserstein-Based High Probability Generalisation Bounds.

Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

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).

Paul Viallard

PhD thesis, 2022

2021

A General Framework for the Derandomization of PAC-Bayesian Bounds.

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

CoRR, 2021

Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound.

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.

Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A PAC-Bayes Analysis of Adversarial Robustness.

Paul Viallard, Guillaume Vidot, Amaury Habrard, Emilie Morvant

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021