Julien Martinelli

According to our database, Julien Martinelli authored at least 20 papers between 2019 and 2026.

Collaborative distances:

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

2026

Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes.

Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki, Quentin Clairon, Mélanie Prague

CoRR, May, 2026

Online Sharp-Calibrated Bayesian Optimization.

Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen, Samuel Kaski

CoRR, May, 2026

In-Context Black-Box Optimization with Unreliable Feedback.

Nicolas Samuel Blumer, Julien Martinelli, Samuel Kaski

CoRR, May, 2026

Time-Aware Latent Space Bayesian Optimization.

Tuan A. Vu, Julien Martinelli, Harri Lähdesmäki

CoRR, March, 2026

2025

In-Context Multi-Objective Optimization.

Xinyu Zhang, Conor Hassan, Julien Martinelli, Daolang Huang, Samuel Kaski

CoRR, December, 2025

Data-driven Discovery of Digital Twins in Biomedical Research.

Clémence Métayer, Annabelle Ballesta, Julien Martinelli

CoRR, August, 2025

Robust and Computation-Aware Gaussian Processes.

Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski

CoRR, May, 2025

Challenges in interpretability of additive models.

Xinyu Zhang, Julien Martinelli, ST John

CoRR, April, 2025

Proxy-informed Bayesian transfer learning with unknown sources.

Sabina J. Sloman, Julien Martinelli, Samuel Kaski

Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

Position: Biology is the Challenge Physics-Informed ML Needs to Evolve.

Julien Martinelli

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

PABBO: Preferential Amortized Black-Box Optimization.

Xinyu Zhang, Daolang Huang, Samuel Kaski, Julien Martinelli

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Human-in-the-loop active learning for goal-oriented molecule generation.

Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski

J. Cheminformatics, December, 2024

Heteroscedastic Preferential Bayesian Optimization with Informative Noise Distributions.

Marshal Arijona Sinaga, Julien Martinelli, Vikas Garg, Samuel Kaski

CoRR, 2024

Bayesian Active Learning in the Presence of Nuisance Parameters.

Sabina J. Sloman, Ayush Bharti, Julien Martinelli, Samuel Kaski

Proceedings of the Uncertainty in Artificial Intelligence, 2024

Learning relevant contextual variables within Bayesian optimization.

Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John, Louis Filstroff, Sabina J. Sloman, Patrick Rinke, Samuel Kaski

Proceedings of the Uncertainty in Artificial Intelligence, 2024

2023

Cost-aware learning of relevant contextual variables within Bayesian optimization.

Julien Martinelli, Ayush Bharti, S. T. John, Armi Tiihonen, Sabina Sloman, Louis Filstroff, Samuel Kaski

CoRR, 2023

Multi-Fidelity Bayesian Optimization with Unreliable Information Sources.

Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

On learning mechanistic models from time series data with applications to personalised chronotherapies. (Sur l'apprentissage automatique de modèles mécanistes à partir de données temporelles avec application aux chronothérapies personnalisées).

Julien Martinelli

PhD thesis, 2022

2021

Model learning to identify systemic regulators of the peripheral circadian clock.

Julien Martinelli, Sandrine Dulong, Xiao-Mei Li, Michèle Teboul, Sylvain Soliman, Francis Lévi, François Fages, Annabelle Ballesta

Bioinform., 2021

2019

On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics.

Julien Martinelli, Jeremy Grignard, Sylvain Soliman, François Fages

Proceedings of the Computational Methods in Systems Biology, 2019