Journal
Alain Durmus, Samuel Gruffaz, Miika Kailas, Eero Saksman, and Matti Vihola. On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers. Annals of Applied Probability (2023). [First author]
Alain Durmus, Samuel Gruffaz, Mareike Hasenpflug, and Daniel Rudolf. Geodesic slice sampling on Riemannian manifolds. Biometrika (2023). [Co-first author]
Samuel Gruffaz and Josua Sassen. Riemannian metric learning: Closer to you than you imagine. arXiv:2503.05321 (2025). Under review to SIAM Review (2025). [First author]
Kyurae Kim, Samuel Gruffaz, Ji Won Park, and Alain Oliviero Durmus. Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped. arXiv:2510.03949 (2025). Submitted to Electronic Journal of Statistics (2025). [Second author]
Samuel Gruffaz, Kyurae Kim, Fares Guehtar, Hadrien Duval-decaix, Pacôme Trautmann. A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets arXiv:2603.18640 (2026) Preprint to be submitted to JMLR [First author]
Esteban A Alarcón-Braga, Samuel Gruffaz, Cécile Delagarde, Axel Roques, Jean-Clément Riff, Laurent Oudre, Clément Dubost. Detecting the Depth of Sedation in the Intensive Care Unit using a 2-channel Electroencephalogram: An analysis with 2 machine learning models. Neuroscience Informatics, 100238 (2025). [Second author]
Proceedings of International Conferences
Gabriel Singer, Samuel Gruffaz, Olivier Vo Van, Nicolas Vayatis, Argyris Kalogeratos. Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints. arXiv:2601.23221 (Submitted to ICML 2026). [Co-first author]
Gaëtan Serré, Pierre Germain, Samuel Gruffaz, Argyris Kalogeratos. Enhancing Exploration in Global Optimization by Noise Injection in the Probability Measures Space. arXiv:2601.22753 (Submitted to ICML 2026). [Co-first author]
Thibaut Germain, Samuel Gruffaz, Charles Truong, Alain Durmus, and Laurent Oudre. Shape analysis for time series. NeurIPS 2024, pp. 95607-95638. [Second author]
Samuel Gruffaz, Kyurae Kim, Alain Durmus, and Jacob Gardner. Stochastic approximation with biased MCMC for expectation maximization. AISTATS 2024, PMLR, pp. 2332-2340. [First author]
Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus, and Laurent Oudre. Personalized Convolutional Dictionary Learning of Physiological Time Series. AISTATS 2025, PMLR pp. 1837-1845. [Second author]
Donovan Morel, Samuel Gruffaz, Damping Wang, Pierre-Paul Vidal, and Lise Haddouk. Multimodal influences of context, anxiety, and personality on the sense of presence in virtual reality. Annual Review of CyberTherapy and Telemedicine, Vol. 23, 2025, pp. 201-206 (CyPsy26) . [Second author]
Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, and Stanley Durrleman. Learning Riemannian metric for disease progression modeling. NeurIPS 2021, pp. 23780-23792. [First author]
Code packages