Upper and Lower Bounds on the Performance of Kernel PCA. - Maxime Haddouche; Benjamin Guedj; Omar Rivasplata; John Shawe-Taylor (CoRR, 2020)

PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. - Maxime Haddouche; Benjamin Guedj; Omar Rivasplata; John Shawe-Taylor (Entropy, 2021)

Online PAC-Bayes Learning. - Maxime Haddouche; Benjamin Guedj (NeurIPS, 2022)

PAC-Bayes with Unbounded Losses through Supermartingales. - Maxime Haddouche; Benjamin Guedj (CoRR, 2022)

Learning via Wasserstein-Based High Probability Generalisation Bounds. - Paul Viallard; Maxime Haddouche; Umut Simsekli; Benjamin Guedj (NeurIPS, 2023)

Optimistic Dynamic Regret Bounds. - Maxime Haddouche; Benjamin Guedj; Olivier Wintenberger (CoRR, 2023)

Wasserstein PAC-Bayes Learning: A Bridge Between Generalisation and Optimisation. - Maxime Haddouche; Benjamin Guedj (CoRR, 2023)

Federated Learning with Nonvacuous Generalisation Bounds. - Pierre Jobic; Maxime Haddouche; Benjamin Guedj (CoRR, 2023)

PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales. - Maxime Haddouche; Benjamin Guedj (Trans. Mach. Learn. Res., 2023)

Tighter Generalisation Bounds via Interpolation. - Paul Viallard; Maxime Haddouche; Umut Simsekli; Benjamin Guedj (CoRR, 2024)

A PAC-Bayesian Link Between Generalisation and Flat Minima. - Maxime Haddouche; Paul Viallard; Umut Simsekli; Benjamin Guedj (ALT, 2025)

Understanding the Generalization Error of Markov algorithms through Poissonization. - Benjamin Dupuis; Maxime Haddouche; George Deligiannidis; Umut Simsekli (CoRR, 2025)

Online Decision-Focused Learning. - Aymeric Capitaine; Maxime Haddouche; Eric Moulines; Michael I. Jordan; Etienne Boursier; Alain Durmus (CoRR, 2025)

Algorithm- and Data-Dependent Generalization Bounds for Score-Based Generative Models. - Benjamin Dupuis; Dario Shariatian; Maxime Haddouche; Alain Durmus; Umut Simsekli (CoRR, 2025)

Logarithmic Smoothing for Adaptive PAC-Bayesian Off-Policy Learning. - Maxime Haddouche; Otmane Sakhi (CoRR, 2025)