Publications associées à Inverse Graph Learning from Social Interactions

Publications associées (10)

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

In this thesis, we study how combinatorial structures and symmetries emerge from analytic properties of graph polynomials and neural networks. Graph polynomials encode combinatorial aspects of the underlying graph in their coefficients and are remarkably u ...

EPFL2025

Enhancing Student Engagement through Social Media-Based Gamification in a UI/UX Design Course

Denis Gillet

Nowadays, incorporating innovative teaching methods that enhance student engagement has become increasingly crucial. In our study, we examine a new pedagogical practice which relies on gamified formative peer assessment and social media to improve student ...

Editorial Universitat Politècnica de València (edUPV)2025

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Ali H. Sayed

To protect both local gradients and estimated parameters in distributed learning, this paper introduces a masked diffusion (MD) strategy, leading to two algorithms: the MD stochastic gradient (MD-SG) and the MD primal-dual stochastic gradient (MPD-SG). The ...

2025

Ion Sieving in Two-Dimensional Membranes from First Principles

Nicola Marzari, Nicéphore Arthur François Bonnet

A first-principles approach for calculating ion separation in solution through two-dimensional (2D) membranes is proposed and applied. Ionic energy profiles across the membrane are obtained first, where solvation effects are simulated explicitly with machi ...

2025

Surface distributions and vertical profiles of trace gases (CO, O3, NO, NO2) in the Arctic wintertime boundary layer using low-cost sensors during ALPACA-2022

Julia Schmale, Andrea Baccarini, Roman Pohorsky

Electrochemical gas sensors (EGSs) have been used to measure the surface distributions and vertical profiles of trace gases in the wintertime Arctic boundary layer during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fair ...

COPERNICUS GESELLSCHAFT MBH2025

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

2024

Teachers, Take Care of the Essential. the Rest Is Story: Using LLM and Social Robots for Content Approaching by Storytelling

Anne-Marie Rusu, Daniel Carnieto Tozadore

Social robots are widely used for educational activities, especially to attract children's attention. As a side effect, pupils' excitement can suppress their focus on the tutors and the content being approached. A potential solution to tackle this issue is ...

IEEE Computer Society2024

Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

Sandro Carrara, Lin Du, Tatsunori Matsumoto

Electrochemical devices and systems are significant for the development of therapeutic drug monitoring (TDM) and personalized therapy. However, electrochemical sensors are usually not that much selective. Therefore, innovative machine learning (ML) approac ...

Institute of Electrical and Electronics Engineers Inc.2024

Causally estimating the effect of YouTube's recommender system using counterfactual bots

Robert West, Manoel Horta Ribeiro

the effects of the algorithm from a user's intentions. Here we propose a method that we call "counterfactual bots" to causally estimate the role of algorithmic recommendations on the consumption of highly partisan content on YouTube. By comparing bots that ...

National Academy of Sciences2024

Source identification via contact tracing in the presence of asymptomatic patients

Miguel-Angel Sanchez Ndoye, Patrick Thiran, Gergely Odor

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algori ...

SPRINGERNATURE2023