most citedTime series forecasting from partial observations via Non-negative Matrix Factorization

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5 papers

cond-mat.mtrl-sci2026

One-dimensional self-organization of water molecules in proton conducting Andersson-Wadsley titanates

Mathilde Arnaud, Guillaume Benas, Narimane Meziani +13

Layered alkali titanates with M 2 Ti 2 O 5 chemical formula (MTO, M=K,Rb) belonging to the Andersson-Wadsley perovskite family spontaneously incorporate water to form MTO.(H 2 O) x…

math.ST2026

Non-asymptotic Tail Bounds for the Kostlan--Shub--Smale Field: Tensor PCA and Spherical -Spin Complexity

Jean-Marc Azaïs, Federico Dalmao, Yohann De Castro

This paper builds a hierarchy of explicit, non-asymptotic tail bounds for the supremum of the Kostlan--Shub--Smale (KSS) random field on the sphere, and applies it to two problems:…

cs.LG2026★ 1 cited

Time series forecasting from partial observations via Non-negative Matrix Factorization

Yohann de Castro, Luca Mencarelli

In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values. In this paper, we introduce the Sliding Mask Method (SMM) for f…

math.OC2026

Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death process

Yohann De Castro, Sébastien Gadat, Clément Marteau

We investigate the global optimization of the objective function arising in continuous sparse regression, specifically the Beurling LASSO (BLASSO), over the space of measures. Whil…

math.ST2026

Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization

Romane Giard, Yohann de Castro, Clément Marteau

This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples. We employ…