5 papers
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…
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…
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…
Physics-informed kernel learning
Nathan Doumèche, Francis Bach, Gérard Biau +1
Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a data-driven term and a partial d…
Physics-informed machine learning as a kernel method
Nathan Doumèche, Francis Bach, Gérard Biau +1
Physics-informed machine learning combines the expressiveness of data-based approaches with the interpretability of physical models. In this context, we consider a general regressi…