2 citations · 3 across the 7 of their papers we have counts for
8 papers
Fast Gradient-Based Approximation of Continuous Dynamic Time Warping
Pierre Houedry, Romain Tavenard, Nicolas Courty +1
Dynamic Time Warping (DTW) is a common choice for measuring the discrepancy between time series. It proceeds by finding an optimal warping between timestamps through dynamic progra…
Multimodal Embeddings for 3D Similarity Search in Semantic Web-of-Things Digital-Twin Platforms
Oussama Zaid, Romaric Gaudel, Hassan Thomas +2
Semantic Web of Things (SWoT) platforms model physical infrastructure as knowledge graphs typed against domain ontologies, enabling expressive structural and logical queries. Howev…
Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning
Sayan Biswas, Antoine Boutet, Davide Frey +7
Decentralized learning (DL) is an emerging machine learning paradigm where nodes collaboratively train models without a central server. However, the collaborative nature of DL make…
Mosaic Learning: A Framework for Decentralized Learning with Model Fragmentation
Sayan Biswas, Davide Frey, Romaric Gaudel +7
Decentralized learning (DL) enables collaborative machine learning (ML) without a central server, making it suitable for settings where training data cannot be centrally hosted. We…
Efficient Matroid Bandit Linear Optimization Leveraging Unimodality
Aurélien Delage, Romaric Gaudel
We study the combinatorial semi-bandit problem under matroid constraints. The regret achieved by recent approaches is optimal, in the sense that it matches the lower bound. Yet, ti…
Unified Privacy Guarantees for Decentralized Learning via Matrix Factorization
Aurélien Bellet, Edwige Cyffers, Davide Frey +3
Decentralized Learning (DL) enables users to collaboratively train models without sharing raw data by iteratively averaging local updates with neighbors in a network graph. This se…
Low-Cost Privacy-Preserving Decentralized Learning
Sayan Biswas, Davide Frey, Romaric Gaudel +5
Decentralized learning (DL) is an emerging paradigm of collaborative machine learning that enables nodes in a network to train models collectively without sharing their raw data or…
Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection
Gwladys Kelodjou, Laurence Rozé, Véronique Masson +4
Machine learning techniques, such as deep learning and ensemble methods, are widely used in various domains due to their ability to handle complex real-world tasks. However, their…