Samuel Gruffaz

According to our database, Samuel Gruffaz authored at least 9 papers between 2021 and 2026.

Collaborative distances:

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

2026

A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets.

Samuel Gruffaz, Kyurae Kim, Fares Guehtar, Hadrien Duval-Decaix, Pacôme Trautmann

CoRR, March, 2026

Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints.

Gabriel Singer, Samuel Gruffaz, Olivier Vo Van, Nicolas Vayatis, Argyris Kalogeratos

CoRR, January, 2026

2025

Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped.

Kyurae Kim, Samuel Gruffaz, Ji Won Park, Alain Oliviero Durmus

CoRR, October, 2025

Riemannian Metric Learning: Closer to You than You Imagine.

Samuel Gruffaz, Josua Sassen

CoRR, March, 2025

From Motor Style Quantification in Time Series To Markov Chain Monte Carlo Within Stochastic Approximation. (De la quantification du style de moteur dans les séries temporelles aux méthodes Monte Carlo par chaîne de Markov dans le cadre de l'approximation stochastique).

Samuel Gruffaz

PhD thesis, 2025

Personalized Convolutional Dictionary Learning of Physiological Time Series.

Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Shape analysis for time series.

Thibaut Germain, Samuel Gruffaz, Charles Truong, Alain Durmus, Laurent Oudre

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Stochastic Approximation with Biased MCMC for Expectation Maximization.

Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner

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

2021

Learning Riemannian metric for disease progression modeling.

Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, Stanley Durrleman

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