← Back to all news
arXiv Machine Learning By Nawel Arab, Mohammed Nabil El Korso, Isabelle Vin, Pascal Larzabal

Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

Read the original on arXiv Machine Learning →

arXiv:2606. 23944v1 Announce Type: cross Abstract: State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture heavy-tailed or outlier-prone measurement noise.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 16

Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm

The paper presents a robust covariance estimation method for data that is both entry-wise contaminated and partially observed. It extends the Student‑t* model by using a stochastic approximation EM algorithm that operates on a reduced latent space, avoiding explicit sampling of latent Gaussian variables while maintaining a likelihood‑based framework. Experiments on synthetic and real sensor data demonstrate that this approach achieves lower Fisher‑Rao error than Gaussian, Student‑t, imputation‑based, and detect‑then‑estimate estimators, especially at moderate to high outlier rates under diffuse contamination.

By Matys Savidan, Mohammed Nabil El Korso
diffusion
More like this →
arXiv Machine Learning
Sep 1

Generative Translation Priors: Bayesian Imaging with Cross-Modality Image Translation

Generative Translation Priors (GTP) is a Bayesian framework that repurposes diffusion-based image-to-image translation models as cross-modality priors for ill‑posed imaging inverse problems. It incorporates target‑domain measurements via likelihood guidance, steering the translation toward the desired posterior distribution. The authors analyze the resulting posterior dynamics, identify an intrinsic bias from likelihood guidance, and propose a ground‑truth‑free metric to estimate this bias, leading to two discretized GTP algorithms that demonstrate high‑fidelity reconstruction in CT and PET imaging using complementary side information.

By Evan Bell, Jiaming Liu, Yifan Chen, Yu Sun
diffusionsafety
More like this →