779 citations
- École PolytechniqueFR888 papers
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40 papers · 1 filter
Estimation of the invariant measure of a multidimensional diffusion from noisy observations
Raphaël Maillet, Grégoire Szymanski
We introduce a new approach for estimating the invariant density of a multidimensional diffusion when dealing with high-frequency observations blurred by independent noise. We cons…
Random features models: a way to study the success of naive imputation
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce…
An analysis of the noise schedule for score-based generative models
Stanislas Strasman, Antonio Ocello, Claire Boyer +2
Score-based generative models (SGMs) aim at estimating a target data distribution by learning score functions using only noise-perturbed samples from the target.Recent literature h…
A Bayesian neural network approach to Multi-fidelity surrogate modelling
Baptiste Kerleguer, Claire Cannamela, Josselin Garnier
This paper deals with surrogate modelling of a computer code output in a hierarchical multi-fidelity context, i.e., when the output can be evaluated at different levels of accuracy…
KL Convergence Guarantees for Score diffusion models under minimal data assumptions
Giovanni Conforti, Alain Durmus, Marta Gentiloni Silveri
Diffusion models are a new class of generative models that revolve around the estimation of the score function associated with a stochastic differential equation. Subsequent to its…
Naive imputation implicitly regularizes high-dimensional linear models
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Two different approaches exist to handle missing values for prediction: either imputation, prior to fitting any predictive algorithms, or dedicated methods able to natively incorpo…