Published on January 26, 2024–Updated on February 20, 2025
Dates
on the February 2, 2024
from 10:30am to 12:00pm
Location
Campus Valrose, Nice
Salle Théâtre
Program
10:30 - 10:40 Greta Damo
Flash presentation
10:40 - 11:00 Xiuheng Wang
Non-parametric Online Change Point Detection on Riemannian Manifolds
Abstract: Non-parametric detection of change points in streaming time series data that belong to Euclidean spaces has been extensively studied in the literature. Nevertheless, when the data belongs to a Riemannian manifold, existing approaches are no longer applicable as they fail to account for the structure and geometry of the manifold. In this paper, we introduce a non-parametric algorithm for online change point detection in manifold-valued data streams. This algorithm monitors the generalized Karcher mean of the data, computed using stochastic Riemannian optimization. We provide theoretical bounds on the detection and false alarm rate performances of the algorithm, using a new result on the non-asymptotic convergence of the stochastic Riemannian gradient descent. We apply our algorithm to two different Riemannian manifolds. Experimental results with both synthetic and real data illustrate the performance of the proposed method.
11:00 - 11:40 Thomas Laloë
Risk measures based on depth functions
Abstract: In this talk, I will present a risk measure based on the level sets of a depth function. I will illustrate this measure with an application to the prediction of power consumption as a function of temperature.
