15 citations · 17 across the 16 of their papers we have counts for
9 papers · 1 filter
From Black-Box Tuning to Guided Optimization via Hyperparameters Interaction Analysis
Moncef Garouani, Ayah Barhrhouj
Hyperparameters tuning is a fundamental, yet computationally expensive, step in optimizing machine learning models. Beyond optimization, understanding the relative importance and i…
Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration
Paul Saves, Pramudita Satria Palar, Muhammad Daffa Robani +6
Complex systems are increasingly explored through simulation-driven engineering workflows that combine physics-based and empirical models with optimization and analytics. Despite t…
XStacking: Explanation-Guided Stacked Ensemble Learning
Moncef Garouani, Ayah Barhrhouj, Olivier Teste
Ensemble Machine Learning (EML) techniques, especially stacking, have been shown to improve predictive performance by combining multiple base models. However, they are often critic…
GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation
Hugo Carlesso, Maria Eliza Patulea, Moncef Garouani +2
Mixup has become a popular augmentation strategy for image classification, yet its naive pixel-wise interpolation often produces unrealistic images that can hinder learning, partic…
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization
Moncef Garouani
Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-lea…
Uncovering the Limitations of Query Performance Prediction: Failures, Insights, and Implications for Selective Query Processing
Adrian-Gabriel Chifu, Sébastien Déjean, Josiane Mothe +3
Query Performance Prediction (QPP) estimates retrieval systems effectiveness for a given query, offering valuable insights for search effectiveness and query processing. Despite ex…
Model Lake: a New Alternative for Machine Learning Models Management and Governance
Moncef Garouani, Franck Ravat, Nathalie Valles-Parlangeau
The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Tradit…
Investigating the Duality of Interpretability and Explainability in Machine Learning
Moncef Garouani, Josiane Mothe, Ayah Barhrhouj +1
The rapid evolution of machine learning (ML) has led to the widespread adoption of complex "black box" models, such as deep neural networks and ensemble methods. These models exhib…
Dense Retrieval for Low Resource Languages -- the Case of Amharic Language
Tilahun Yeshambel, Moncef Garouani, Serge Molina +1
This paper reports some difficulties and some results when using dense retrievers on Amharic, one of the low-resource languages spoken by 120 millions populations. The efforts put…