Publications

Julien Martinelli, Ayush Bharti, S.T. John, Armi Tiihonen, Sabina Sloman, Louis Filstroff, Samuel Kaski (2023). Cost-aware learning of relevant contextual variables within Bayesian optimization. *Preprint. Under review..

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Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski (2022). Multi-Fidelity Bayesian Optimization with Unreliable Information Sources. *Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.

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Julien Martinelli, Jeremy Grignard, Sylvain Soliman, Annabelle Ballesta, François Fages (2022). Reactmine, a search algorithm for inferring chemical reaction networks from time series data. Preprint.

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Clémence Frioux, Sylvie Huet, Simon Labarthe, Julien Martinelli, Thibault Malou, David Sherman, Marie-Luce Taupin, Pablo Ugalde-Salas (2022). Accelerating metabolic models evaluation with statistical metamodels - application to Salmonella infection models. ESAIM Proceedings & Surveys.

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Julien Martinelli, Sandrine Dulong, Xiao-Mei Li, Sylvain Soliman, Francis Lévi, François Fages, and Annabelle Ballesta (2021). Model Learning to Identify Systemic Regulators of the Peripheral Circadian Clock. ECCB'21: Proceedings of the twentieth european conference on Computational Biology.

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Janina Hesse, Julien Martinelli, Ouda Aboumanify, Annabelle Ballesta, Angela Relógio (2021). A mathematical model of the circadian clock and drug pharmacology to optimize irinotecan administration timing in colorectal cancer. Computational and Structural Biology.

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Julien Martinelli, Jeremy Grignard, Sylvain Soliman, and François Fages (2019). On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics. CMSB'19: Proceedings of the seventeenth international conference on Computational Methods for Systems Biology.

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Julien Martinelli, Jeremy Grignard, Sylvain Soliman, François Fages (2019). A Statistical Unsupervised Learning Algorithm for Inferring Reaction Networks from Time Series Data. ICML Workshop on Computational Biology.

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