15 citations · 17 across the 16 of their papers we have counts for
16 papers
Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
Pramudita Satria Palar, Paul Saves, Muhammad Daffa Robani +6
The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing…
IPatch: A Multi-Resolution Transformer Architecture for Robust Time-Series Forecasting
Aymane Harkati, Moncef Garouani, Olivier Teste +2
Accurate forecasting of multivariate time series remains challenging due to the need to capture both short-term fluctuations and long-range temporal dependencies. Transformer-based…
Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations
Hajar Dekdegue, Moncef Garouani, Josiane Mothe +1
Interpreting the decision-making process of deep convolutional neural networks remains a central challenge in achieving trustworthy and transparent artificial intelligence. Explain…
Improving Neural Retrieval with Attribution-Guided Query Rewriting
Moncef Garouani, Josiane Mothe
Neural retrievers are effective but brittle: underspecified or ambiguous queries can misdirect ranking even when relevant documents exist. Existing approaches address this brittlen…
AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning
Tilahun Yeshambel, Moncef Garouani, Josiane Mothe
Neural retrieval and GPT-style generative models rely on large, high-quality supervised data, which is still scarce for low-resource languages such as Amharic. We release an Amhari…
Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity
Jordan Levy, Paul Saves, Moncef Garouani +2
Unsupervised anomaly detection is a challenging problem due to the diversity of data distributions and the lack of labels. Ensemble methods are often adopted to mitigate these chal…
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…
Can we predict QPP? An approach based on multivariate outliers
Adrian-Gabriel Chifu, Sébastien Déjean, Moncef Garouani +3
Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents. While state-of-the-art predictors offer a certain…