Maryam Boubekraoui

Maryam Boubekraoui, Giordano d'Aloisio, Antinisca Di Marco

The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigation in binary classification tasks, the issue remains under-explored in multi-class classification settings. To address this limitation, in this paper, we first formulate the problem of fair learning in multi-class classification as a multi-objective problem between effectiveness (i.e., prediction correctness) and multiple linear fairness constraints. Next, we propose a Generalised Exponentiated Gradient (GEG) algorithm to solve this task. GEG is an in-processing algorithm that enhances fairness in binary and multi-class classification settings under multiple fairness definitions. We conduct an extensive empirical evaluation of GEG against six baselines across seven multi-class and three binary datasets, using four widely adopted effectiveness metrics and three fairness definitions. GEG overcomes existing baselines, with fairness improvements up to 92% and a decrease in accuracy up to 14%.

Maryam Boubekraoui, Ridwane Tahiri

Modeling complex multiway relationships in large-scale networks is becoming more and more challenging in data science. The multilinear PageRank problem, arising naturally in the study of higher-order Markov chains, is a powerful framework for capturing such interactions, with applications in web ranking, recommendation systems, and social network analysis. It extends the classical Google PageRank model to a tensor-based formulation, leading to a nonlinear system that captures multi-way dependencies between states. Newton-based methods can achieve local quadratic convergence for this problem, but they require solving a large linear system at each iteration, which becomes too costly for large-scale applications. To address this challenge, we present an accelerated Newton-GMRES method that leverages Krylov subspace techniques to approximate the Newton step without explicitly forming the large Jacobian matrix. We further employ vector extrapolation methods, including Minimal Polynomial Extrapolation (MPE), Reduced Rank Extrapolation (RRE), and Anderson Acceleration (AA), to improve the convergence rate and enhance numerical stability. Extensive experiments on synthetic and real-world data demonstrate that the proposed approach significantly outperforms classical Newton-based solvers in terms of efficiency, robustness, and scalability.