Publication
Decentralized Decision-Making Over Multi-Task Networks
Related publications (32)
Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning
Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...
EPFL2024Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub
Felix Schürmann, Armando Romani, Michele Migliore, Luca Leonardo Bologna
To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...
2023Multi-robot task allocation for safe planning against stochastic hazard dynamics
Maryam Kamgarpour, Orcun Karaca
We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...
2023Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations
Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...
EPFL2023Privacy-Enhancing Optical Embeddings for Lensless Classification
Martin Vetterli, Eric Bezzam, Matthieu Martin Jean-André Simeoni
Lensless imaging can provide visual privacy due to the highly multiplexed characteristic of its measurements. However, this alone is a weak form of security, as various adversarial attacks can be designed to invert the one-to-many scene mapping of such cam ...
2022Meta Navigation Functions: Adaptive Associations for Coordination of Multi-Agent Systems
In this paper, we introduce a new class of potential fields, i.e., meta navigation functions (MNFs) to coordinate multi-agent systems. Thanks to the MNF formulation, agents can contribute to each other's coordination via partial and/or total associations, ...
IEEE2022Opinion Formation over Adaptive Networks
An adaptive network consists of multiple communicating agents, equipped with sensing and learning abilities that allow them to extract meaningful information from measurements. The objective of the network is to solve a global inference problem in a decent ...
EPFL2022Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models
François Maréchal, Ivan Daniel Kantor, Julia Granacher
Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...
2021Self-learning surrogate models in superstructure optimization
François Maréchal, Ivan Daniel Kantor, Julia Granacher, Michel Lopez
In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid t ...
Elsevier2021Adaptation And Learning In Multi-Task Decision Systems
Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging scen ...
IEEE2020