5 papers
Explaining f-Divergence-Based Regularization via Local Curvature and Sharpness-Aware Minimization
Nour Jamoussi, Marios Kountouris
Divergence-based regularization and Sharpness-Aware Minimization (SAM) are two prominent approaches for improving generalization in deep learning, both motivated by robustness to p…
DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton
Nour Jamoussi, Ikram Dridi, Giuseppe Serra +1
Differential privacy provides formal privacy guarantees for training neural networks on sensitive data, while Bayesian deep learning offers a principled framework for uncertainty-a…
Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning
Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou +1
Bayesian Federated Learning (BFL) combines uncertainty modeling with decentralized training, enabling the development of personalized and reliable models under data heterogeneity a…
A Conformal Predictive Measure for Assessing Catastrophic Forgetting
Ioannis Pitsiorlas, Nour Jamoussi, Marios Kountouris
This work introduces a novel methodology for assessing catastrophic forgetting (CF) in continual learning. We propose a new conformal prediction (CP)-based metric, termed the Confo…
Information-Geometric Barycenters for Bayesian Federated Learning
Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou +1
Federated learning (FL) is a widely used and impactful distributed optimization framework that achieves consensus through averaging locally trained models. While effective, this ap…