3 papers
cs.LG2026
A Spectral Framework for Closed-Form Relative Density Estimation
Francis Bach
We propose a closed-form spectral framework for relative log-density estimation in linearly parameterized probabilistic models, including unnormalized and conditional models. This…
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
Forecasting time series with constraints
Nathan Doumèche, Francis Bach, Ãloi 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…