Catégorie : Séminaires projet SiMul

19 janvier 2024 @ 15h00 – 16h00 –

Matrix and tensor decomposition plays a crucial role in addressing various real-world problems related to topics such as statistical inference, data acquisition, and data restoration. In this talk, we start with low-rank matrix/tensor models for the data completion problem [1,2]. We tackle this problem in the framework of low-rank matrix/tensor decomposition with a least-squares model. These rank-constrained problems are known not only for their low computational complexity but also the capability of extracting the most important information in the data. We discuss a type of Riemannian gradient-based algorithms that exploit the structure of these rank-constrained models. Secondly, we present a novel application of low-rank matrix methods in the context of causal structure learning. We will show how low-rank matrix decomposition, in combination with a sparse mask operator, can be used to efficiently find directed acyclic graphs (DAGs) proximal to a given graph (with cycles). Furthermore, for learning causal DAGs from observational data, we present a sparse matrix decomposition method [4] and discuss its efficiency through experiments on synthetic and real-world data.
[1] S. Dong, P.-A. Absil, and K. A. Gallivan, Riemannian gradient descent methods for graph-regularized matrix completion. Linear Algebra and its Applications 623 (2021), 193-235
[2] S. Dong, B. Gao, Y. Guan, and F. Glineur, New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition, SIAM Journal on Matrix Analysis and Applications 43 (2) (2022), 840-866
[3] S. Dong and M. Sebag, From graphs to DAGs: a low-complexity model and a scalable algorithm, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2022
[4] S. Dong, K. Uemura, A. Fujii, S. Chang, Y. Koyanagi, K. Maruhashi, and M. Sebag, Learning large causal structures from inverse covariance matrix via matrix decomposition, arXiv preprint arXiv:2211.14221, 2023

9 février 2024 @ 14h00 – 15h30 – Speaker: Valentin Leplat (SkolTech, Moscow, Russia) Webpage: https://sites.google.com/view/valentinleplat/ Date: February, 9th 2024, 14h-15h Title: Introduction to Deep Nonnegative Matrix Factorization and Stochastic Optimization with heavy-tails Abstract: Part 1: Deep Nonnegative Matrix Factorization with β-Divergences Our first topic revolves around the Deep Nonnegative Matrix Factorization (deep NMF), a novel and promising facet of unsupervised learning. Deep […]

11 mars 2024 @ 14h00 – 15h30 – Speaker: Linus Bleistein (Inria Paris) Website: https://www.linusbleistein.com/ Title: Dynamical Survival Analysis with Controlled Latent States Abstract: We consider the task of learning individual specific intensities of survival processes from static and longitudinal data. Modeling the intensities as solutions to non-parametric unknown differential equations allows us to provide a precise bias-variance decomposition of a signature-based estimator. […]

18 mars 2024 @ 10h00 – 11h30 – Orateur: Marc Offroy (LIEC, Nancy) Titre de l’exposé: Extraction de signatures spectrales en imagerie Raman par des outils de chimiométrie pour la caractérisation moléculaire d’un échantillon archéologique complexe : un fragment de mosaïque datant de la période de l’oppidum (période Romaine de la seconde moitié du IIe siècle avant J.-C. à la fin du Ier […]

25 mars 2024 @ 14h00 – 15h00 – Speaker: Paul Catala (Helmholtz Munich, TUM, Germany) Title: An Approximate Joint Diagonalization Algorithm for Off-the-Grid Sparse Recovery Many problems in imaging and data science require to reconstruct, from partial observations, highly concentrated signals, such as pointwise sources or contour lines. This work introduces a novel algorithm for recovering measures supported on such structured domains, given […]

15 avril 2024 @ 15h00 – 16h00 – Speaker: Vicente Zarzoso (Université Côte d’Azur, Nice) Website: https:/webusers.i3s.unice.fr/~zarzoso/ Title: Tensor decomposition of ECG records for persistent atrial fibrillation analysis Considered as the last great frontier of cardiac electrophysiology, atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice, responsible for high hospitalization rates and a significant proportion of brain strokes in […]