# Modal Seminar (2021-2022)

Usual day: Tuesday at 11.00.

Place: Inria Lille – Nord Europe.

How to get thereen françaisin english.

OrganizersHemant Tyagi

Calendar feediCalendar (hosted by the seminars platform of University of Lille)

Most slides are available: check past sessions and archives.

## Upcoming

Date: Nov 23, 2021 (Tuesday) at 11.00 (Online seminar)
Affiliation:   EPFL, Switzerland
Title:  Learning over graphs: A signal processing complement
Abstract: The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this talk, we will highlight how some GSP concepts and tools, such as graph filters and transforms, lead to the development of novel graph-based machine learning algorithms for representation learning and topology inference. Finally, we will show some illustrative applications in computer vision, and healthcare.
Date: Nov 9, 2021 (Tuesday) at 11.00 (Online seminar)
Affiliation:   University Grenoble-Alpes
Abstract:  Many applications such as recommendation systems or sports tournaments involve pairwise comparisons within a collection of $n$ items, the goal being to aggregate the binary outcomes of the comparisons in order to recover the latent strength and/or global ranking of the items. In recent years, this problem has received significant interest from a theoretical perspective with a number of methods being proposed, along with associated statistical guarantees under the assumption of a suitable generative model.
While these results typically collect the pairwise comparisons as one comparison graph $G$, however in many applications — such as the outcomes of soccer matches during a tournament — the nature of pairwise outcomes can evolve with time. Theoretical results for such a dynamic setting are relatively limited compared to the aforementioned static setting. We study an extension of the classic BTL (Bradley-Terry-Luce) model for the static setting to our dynamic setup under the assumption that the probabilities of the pairwise outcomes evolve smoothly over the time domain $[0,1]$. Given a sequence of comparison graphs $(G_{t’})_{t’ \in \mathcal{T}}$ on a regular grid $\mathcal{T} \subset [0,1]$, we aim at recovering the latent strengths of the items $w_t^* \in \mathbb{R}^n$ at any time $t \in [0,1]$. To this end, we adapt the Rank Centrality method — a popular spectral approach for ranking in the static case — by locally averaging the available data on a suitable neighborhood of $t$. When $(G_{t’})_{t’ \in \mathcal{T}}$ is a sequence of Erdös-Renyi graphs, we provide non-asymptotic $\ell_2$ and $\ell_{\infty}$ error bounds for estimating $w_t^*$ which in particular establishes the consistency of this method in terms of $n$, and the grid size $|\mathcal{T}|$. We also complement our theoretical analysis with experiments on real and synthetic data. (joint work with Hemant Tyagi)