Reposted by Timothée DevergneCSML IIT Lab @pontilgroup.bsky.social · 17/12/2025Almost 5 years in the making... "Hyperparameter Optimization in Machine Learning" is finally out! 📘 We designed this monograph to be self-contained, covering: Grid, Random & Quasi-random search, Bayesian & Multi-fidelity optimization, Gradient-based methods, Meta-learning. arxiv.org/abs/2410.22854 0139
Reposted by Timothée DevergneJean-Philip Piquemal @jppiquem.bsky.social · 26/03/2025#compchem Good read: Slow dynamical modes from static averages #compchemsky doi.org/10.1063/5.02...doi.orgSlow dynamical modes from static averagesIn recent times, efforts have been made to describe the evolution of a complex system not through long trajectories but via the study of probability distributio 062
Timothée Devergne @tdevergne.bsky.social · 26/03/2025I am excited to share with you our new work with Vladimir Kostic, @pontilgroup.bsky.social and Michele Parrinello doi.org/10.1063/5.02...doi.orgSlow dynamical modes from static averagesIn recent times, efforts have been made to describe the evolution of a complex system not through long trajectories but via the study of probability distributio 130
Reposted by Timothée DevergneCSML IIT Lab @pontilgroup.bsky.social · 15/01/20251/ 🚀 Over the past two years, our team, CSML, at IIT, has made significant strides in the data-driven modeling of dynamical systems. Curious about how we use advanced operator-based techniques to tackle real-world challenges? Let’s dive in! 🧵👇 153