Publication

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

Publications associées (29)

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Caching and Neutrality

Pavlos Nikolopoulos, Muhammad Abdullah, Aikaterini Argyraki

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

ACM Association for Computing Machinery2023

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Nirupam Gupta, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Danilo Saccani, Melanie Nicole Zeilinger

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

IEEE2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

EPFL2023

Worst-Case Delay Analysis of Time-Sensitive Networks with Network Calculus

Seyed Mohammadhossein Tabatabaee

Time-sensitive networks, as in the context of IEEE Time-Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet), offer deterministic services with guaranteed bounded latency in order to support safety-critical applications. In this thesis, we ...

EPFL2023

Homogenization results for the generator of multiscale Langevin dynamics in weighted Sobolev spaces

Andrea Zanoni

We study the homogenization of the Poisson equation with a reaction term and of the eigenvalue problem associated to the generator of multiscale Langevin dynamics. Our analysis extends the theory of two-scale convergence to the case of weighted Sobolev spa ...

OXFORD UNIV PRESS2023

Bayes Security: A Not So Average Metric

Carmela González Troncoso, Giovanni Cherubin

Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system's performance. In this pa ...

IEEE2023