Search Results for author: Jimmy Olsson

Found 9 papers, 4 papers with code

Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors

no code implementations18 Mar 2024 Yazid Janati, Alain Durmus, Eric Moulines, Jimmy Olsson

In this work, we take a different approach and utilize the specific structure of the DDM prior to define a set of intermediate and simpler posterior sampling problems, resulting in a lower approximation error compared to previous methods.

Denoising Image Restoration

Importance sampling for online variational learning

no code implementations5 Feb 2024 Mathis Chagneux, Pierre Gloaguen, Sylvain Le Corff, Jimmy Olsson

This article addresses online variational estimation in state-space models.

Online Variational Sequential Monte Carlo

1 code implementation19 Dec 2023 Alessandro Mastrototaro, Jimmy Olsson

Being the most classical generative model for serial data, state-space models (SSM) are fundamental in AI and statistical machine learning.

Variational Inference

State and parameter learning with PaRIS particle Gibbs

no code implementations2 Jan 2023 Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson

The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models.

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling

1 code implementation13 Jul 2022 Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson

This method is a wrapper in the sense that it uses the same proposal samples and importance weights as SNIS, but makes clever use of iterated sampling--importance resampling (ISIR) to form a bias-reduced version of the estimator.

Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning

no code implementations24 Feb 2021 Tianfang Zhang, Rasmus Bokrantz, Jimmy Olsson

We show that the features extracted by the variational autoencoder capture geometric information of substantial relevance to the dose statistic prediction problem and are related to dose statistics in a more regularized fashion than hand-crafted features.

A similarity-based Bayesian mixture-of-experts model

no code implementations3 Dec 2020 Tianfang Zhang, Rasmus Bokrantz, Jimmy Olsson

We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k-nearest neighbors algorithm.

Sequential sampling of junction trees for decomposable graphs

1 code implementation2 Jun 2018 Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios

On the other hand, the junction-tree collapser provides a complementary operation for removing vertices in the underlying decomposable graph of a junction tree, while maintaining the junction tree property.

Statistics Theory Discrete Mathematics Combinatorics Statistics Theory

Bayesian inference in decomposable graphical models using sequential Monte Carlo methods

1 code implementation31 May 2018 Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios

The theoretical properties of the algorithm are investigated, showing in particular that the refreshment step improves the algorithm performance in terms of asymptotic variance of the estimated distribution.

Statistics Theory Statistics Theory

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