3 citations · 3 across the 23 of their papers we have counts for
24 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…
Neilson's Weak vs. Strong Loss Aversion: A Characterization and a Generalized CPT-Utility Function
Symeon Vaidanis, Marios Kountouris
In multi-objective and multi-criteria decision-making under risk, especially in settings involving individual behavior, risk-aware analysis based on subjective evaluation has becom…
Rate-Distortion-Perception Theory: Redefining the Fundamental Limits of Information Representation
Photios A. Stavrou, Giuseppe Serra, Marios Kountouris
Classical rate-distortion (RD) theory has long established the fundamental limits of lossy compression by quantifying the minimum number of bits required to represent a source unde…
Sequential Fairness Auditing with Limited Output Access
Ioannis Pitsiorlas, Martha V. Sourla, Marios Kountouris
External evaluations are becoming increasingly central to the governance of AI systems. In practice, however, independent auditors often have limited access to deployed models and…
Probabilistic Risk Sensitivity and Loss Aversion in Cumulative Prospect Theory
Symeon Vaidanis, Marios Kountouris
This paper develops a binary-gamble framework for characterizing risk sensitivity and loss aversion in Cumulative Prospect Theory (CPT). The proposed probabilistic risk-sensitivity…
Copula-Induced Correntropy for Robust Conjugate Gradient Learning
Farshad Rostami Ghadi, F. Javier Lopez-Martinez, David Morales-Jimenez +2
Robust learning in the presence of non-Gaussian and statistically dependent noise remains a fundamental challenge in signal processing and adaptive systems. Although information-th…
Rényi Rate-Distortion-Perception-Privacy Tradeoff under Indirect Observation
Jiahui Wei, Marios Kountouris
We introduce a Rényi Rate-Distortion-Perception-Privacy (R-RDPP) framework for indirect source coding. A latent source~ is correlated with a private attribute~, and the encod…
MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
Jeanne Monnier, Thomas George, Frédéric Guyard +2
Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specific bias metrics. Existing meth…
Entanglement Fidelity in Standard Quantum Channels
Niccolò Zanieri, Marios Kountouris
Entanglement fidelity quantifies how well a quantum channel preserves the correlations between a transmitted system and an inaccessible reference system. We derive closed-form expr…
Pilot-Free Optimal Control over Wireless Networks: A Control-Aided Channel Prediction Approach
Minjie Tang, Zunqi Li, Photios A. Stavrou +1
A recurring theme in optimal controller design for wireless networked control systems (WNCS) is the reliance on real-time channel state information (CSI). However, acquiring accura…
Multi-Sensor Scheduling for Remote State Estimation over Wireless MIMO Fading Channels with Semantic Over-the-Air Aggregation
Minjie Tang, Photios A. Stavrou, Marios Kountouris
In this work, we study multi-sensor scheduling for remote state estimation over wireless multiple-input multiple-output (MIMO) fading channels using a novel semantic over-the-air (…
Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
Babacar Toure, Dimitrios Tsilimantos, Omid Esrafilian +1
Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. I…
On the Rényi Rate-Distortion-Perception Function and Functional Representations
Jiahui Wei, Marios Kountouris
We extend the Rate-Distortion-Perception (RDP) framework to the Rényi information-theoretic regime, utilizing Sibson's -mutual information to characterize the fundamental limits…
Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory
Martha V. Sourla, Giuseppe Serra, Photios A. Stavrou +1
The problem of estimating the information rate distortion perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and sema…
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…
Information Entropy-Based Scheduling for Communication-Efficient Decentralized Learning
Jaiprakash Nagar, Zheng Chen, Marios Kountouris +1
This paper addresses decentralized stochastic gradient descent (D-SGD) over resource-constrained networks by introducing node-based and link-based scheduling strategies to enhance…
Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
The problem of resource allocation in goal-oriented semantic communication with semantic-aware utilities and subjective risk perception is studied here. By linking information impo…
Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perc…
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…
CSI-Free Low-Complexity Remote State Estimation over Wireless MIMO Fading Channels using Semantic Analog Aggregation
Minjie Tang, Photios A. Stavrou, Marios Kountouris
In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multiple-output (MIMO) channels without requiring prior knowledge of channel…
On the Rate-Distortion-Perception Function for Gaussian Processes
Giuseppe Serra, Photios A. Stavrou, Marios Kountouris
In this paper, we investigate the rate-distortion-perception function (RDPF) of a source modeled by a Gaussian Process (GP) on a measure space under mean squared error (MSE) di…
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…
Alternating Minimization Schemes for Computing Rate-Distortion-Perception Functions with -Divergence Perception Constraints
Giuseppe Serra, Photios A. Stavrou, Marios Kountouris
We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception…