Anomaly Detection in Vehicle-to-Infrastructure Communications. - Michele Russo; Maxime Labonne; Alexis Olivereau; Mohammad Rmayti (VTC Spring, 2018)

A Cascade-structured Meta-Specialists Approach for Neural Network-based Intrusion Detection. - Maxime Labonne; Alexis Olivereau; Baptiste Polvé; Djamal Zeghlache (CCNC, 2019)

Predicting Bandwidth Utilization on Network Links Using Machine Learning. - Maxime Labonne; Charalampos Chatzinakis; Alexis Olivereau (EuCNC, 2020)

Unsupervised Protocol-based Intrusion Detection for Real-world Networks. - Maxime Labonne; Alexis Olivereau; Baptiste Polvé; Djamal Zeghlache (ICNC, 2020)

Priority Flow Admission and Routing in SDN: Exact and Heuristic Approaches. - Jorge López; Maxime Labonne; Claude Poletti; Dallal Belabed (NCA, 2020)

Short-Term Flow-Based Bandwidth Forecasting using Machine Learning. - Maxime Labonne; Jorge López; Claude Poletti; Jean-Baptiste Munier (CoRR, 2020)

Anomaly-based network intrusion detection using machine learning. (Détection d'intrusion réseau par anomalies avec apprentissage automatique). - Maxime Labonne (2020)

Toward Formal Data Set Verification for Building Effective Machine Learning Models. - Jorge López; Maxime Labonne; Claude Poletti (KDIR, 2021)

WIP: Short-Term Flow-Based Bandwidth Forecasting using Machine Learning. - Maxime Labonne; Jorge López; Claude Poletti; Jean-Baptiste Munier (WOWMOM, 2021)

Dynamic Link Network Emulation and Validation of Execution Datasets. - Erick Petersen; Jorge López; Natalia Kushik; Maxime Labonne; Claude Poletti; Djamal Zeghlache (ENASE (Selected Papers), 2022)

Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection. - Maxime Labonne; Sean J. Moran (CoRR, 2023)

An Unsupervised Method for Estimating Class Separability of Datasets with Application to LLMs Fine-Tuning. - Najah Ghalyan; Kostis Gourgoulias; Yash Satsangi; Sean J. Moran; Maxime Labonne; Joseph Sabelja (CoRR, 2023)

Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation. - Thomas Gauthier-Caron; Shamane Siriwardhana; Elliot Stein; Malikeh Ehghaghi; Charles Goddard; Mark McQuade; Jacob Solawetz; Maxime Labonne (CoRR, 2024)

The path forward for large language models in medicine is open. - Lars Riedemann; Maxime Labonne; Stephen Gilbert (npj Digit. Medicine, 2024)

Estimating class separability of text embeddings with persistent homology. - Kostis Gourgoulias; Najah Ghalyan; Maxime Labonne; Yash Satsangi; Sean J. Moran; Joseph Sabelja (Trans. Mach. Learn. Res., 2024)

On Iterative Evaluation and Enhancement of Code Quality Using GPT-4o. - Rundong Liu; Andre Frade; Amal Vaidya; Maxime Labonne; Marcus Kaiser; Bismayan Chakrabarti; Jonathan Budd; Sean J. Moran (CoRR, 2025)