Publications associées à Source identification via contact tracing in the presence of asymptomatic patients

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

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

Best of Both Worlds: Regret Minimization versus Minimax Play

Volkan Cevher, Luca Viano

In this paper, we investigate the existence of online learning algorithms with bandit feedback that simultaneously guarantee O(1) regret compared to a given comparator strategy, and Õ(√ T) regret compared to any fixed strategy, where T is the number of rou ...

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

Inverse Graph Learning from Social Interactions

Valentina Shumovskaia

The social learning paradigm for distributed hypothesis testing involves a collection of agents interacting over a graph, each observing streaming data that provide evidence about the state of the environment. These agents perform a series of local Bayesia ...

EPFL2024

Aircraft Trajectory Planning for Climate Hotspot Avoidance Considering Air Traffic Complexity: A Constrained Multi-Agent Reinforcement Learning Approach

Maryam Kamgarpour, Tingting Ni

Planning aircraft trajectories to avoid climate-sensitive areas poses operational challenges, including increased traffic complexity and potential safety risks. This study presents a framework designed to plan operationally feasible climate-friendly routes ...

2024

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

Data-centric workloads with MPI_Sort

Gilles Fourestey

Sorting is a fundamental task in computing and plays a central role in information technology. The advent of rack-scale and warehouse-size data processing shaped the architecture of data analysis platforms towards supercomputing. In turn, established techn ...

2024

Euclid preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Frédéric Courbin, Georges Meylan, Georgios Vernardos, Benjamin Yvan Alexandre Clement

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-le ...

Edp Sciences S A2024

How can LLMs transform the robotic design process?

Josephine Anna Eleanor Hughes, Francesco Stella

NATURE PORTFOLIO2023