Showing stat.MLShow all
3 papers · 1 filter
stat.ML2025
Fast kernel methods: Sobolev, physics-informed, and additive models
Nathan Doumèche, Francis Bach, Gérard Biau +1
Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size n limits their use on large-scale datasets. In this work, we introduce a sc…
stat.ML2025
Physics-informed machine learning: A mathematical framework with applications to time series forecasting
Nathan Doumèche
Physics-informed machine learning (PIML) is an emerging framework that integrates physical knowledge into machine learning models. This physical prior often takes the form of a par…
stat.ML2025
Forecasting time series with constraints
Nathan Doumèche, Francis Bach, Éloi Bedek +3
Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have in…