2 citations · 2 across the 6 of their papers we have counts for
6 papers
Time-Aware Latent Space Bayesian Optimization
Tuan A. Vu, Julien Martinelli, Harri Lähdesmäki
Latent-space Bayesian optimization (LSBO) extends Bayesian optimization to structured domains, such as molecular design, by searching in the continuous latent space of a generative…
Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
Julien Martinelli
Physics-Informed Machine Learning (PIML) has successfully integrated mechanistic understanding into machine learning, particularly in domains governed by well-known physical laws.…
Data-driven Discovery of Digital Twins in Biomedical Research
Clémence Métayer, Annabelle Ballesta, Julien Martinelli
Recent technological advances have expanded the availability of high-throughput biological datasets, enabling the reliable design of digital twins of biomedical systems or patients…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
Petrus Mikkola, Julien Martinelli, Louis Filstroff +1
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate…
Reactmine: a statistical search algorithm for inferring chemical reactions from time series data
Julien Martinelli, Jeremy Grignard, Sylvain Soliman +2
Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level.…