SRide: A Privacy-Preserving Ridesharing System. - Ulrich Matchi Aïvodji; Kévin Huguenin; Marie-José Huguet; Marc-Olivier Killijian (WISEC, 2018)

Privacy-Enhancing Technologies for Ridesharing. (Technologies respectueuses de la vie privée pour le covoiturage). - Ulrich Aïvodji (2018)

Fairwashing: the risk of rationalization. - Ulrich Aïvodji; Hiromi Arai; Olivier Fortineau; Sébastien Gambs; Satoshi Hara 001; Alain Tapp (ICML, 2019)

IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning. - Ulrich Matchi Aïvodji; Sébastien Gambs; Alexandre Martin (IEEE Symposium on Security and Privacy Workshops, 2019)

Privacy of trajectory micro-data : a survey. - Marco Fiore 001; Panagiota Katsikouli; Elli Zavou; Mathieu Cunche; Françoise Fessant; Dominique Le Hello; Ulrich Matchi Aïvodji; Baptiste Olivier; Tony Quertier; Razvan Stanica (CoRR, 2019)

Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach. - Ulrich Aïvodji; François Bidet; Sébastien Gambs; Rosin Claude Ngueveu; Alain Tapp (CoRR, 2019)

Learning Fair Rule Lists. - Ulrich Aïvodji; Julien Ferry; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CoRR, 2019)

GAMIN: An Adversarial Approach to Black-Box Model Inversion. - Ulrich Aïvodji; Sébastien Gambs; Timon Ther (CoRR, 2019)

Model extraction from counterfactual explanations. - Ulrich Aïvodji; Alexandre Bolot; Sébastien Gambs (CoRR, 2020)

Privacy in trajectory micro-data publishing: a survey. - Marco Fiore 001; Panagiota Katsikouli; Elli Zavou; Mathieu Cunche; Françoise Fessant; Dominique Le Hello; Ulrich Matchi Aïvodji; Baptiste Olivier; Tony Quertier; Razvan Stanica (Trans. Data Priv., 2020)

FairCORELS, an Open-Source Library for Learning Fair Rule Lists. - Ulrich Aïvodji; Julien Ferry; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CIKM, 2021)

Learning-based Incast Performance Inference in Software-Defined Data Centers. - Kokouvi Bénoît Nougnanke; Yann Labit; Marc Bruyere; Simone Ferlin; Ulrich Aïvodji (ICIN, 2021)

Characterizing the risk of fairwashing. - Ulrich Aïvodji; Hiromi Arai; Sébastien Gambs; Satoshi Hara 001 (NeurIPS, 2021)

Local Data Debiasing for Fairness Based on Generative Adversarial Training. - Ulrich Aïvodji; François Bidet; Sébastien Gambs; Rosin Claude Ngueveu; Alain Tapp (Algorithms, 2021)

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. - Jessica Dai; Sohini Upadhyay; Ulrich Aïvodji; Stephen H. Bach; Himabindu Lakkaraju (AIES, 2022)

Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists. - Ulrich Aïvodji; Julien Ferry; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CPAIOR, 2022)

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection. - Ali Shahin Shamsabadi; Mohammad Yaghini; Natalie Dullerud; Sierra Calanda Wyllie; Ulrich Aïvodji; Aisha Alaagib; Sébastien Gambs; Nicolas Papernot (NeurIPS, 2022)

Fooling SHAP with Stealthily Biased Sampling. - Gabriel Laberge; Ulrich Aïvodji; Satoshi Hara 001; Mario Marchand; Foutse Khomh (ICLR, 2023)

Exploiting Fairness to Enhance Sensitive Attributes Reconstruction. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (SaTML, 2023)

SoK: Taming the Triangle - On the Interplays between Fairness, Interpretability and Privacy in Machine Learning. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CoRR, 2023)

ML-Based Performance Modeling in SDN-Enabled Data Center Networks. - Kokouvi Bénoît Nougnanke; Yann Labit; Marc Bruyere; Ulrich Aïvodji; Simone Ferlin (IEEE Trans. Netw. Serv. Manag., 2023)

Improving fairness generalization through a sample-robust optimization method. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (Mach. Learn., 2023)

Probabilistic Dataset Reconstruction from Interpretable Models. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (SaTML, 2024)

Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists. - Timothée Ly; Julien Ferry; Marie-José Huguet; Sébastien Gambs; Ulrich Aïvodji (CoRR, 2024)

Adaptive Group Robust Ensemble Knowledge Distillation. - Patrik Joslin Kenfack; Ulrich Aïvodji; Samira Ebrahimi Kahou (CoRR, 2024)

Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods. - Julien Ferry; Gabriel Laberge; Ulrich Aïvodji (Trans. Mach. Learn. Res., 2024)

A Survey on Fairness Without Demographics. - Patrik Joslin Kenfack; Samira Ebrahimi Kahou; Ulrich Aïvodji (Trans. Mach. Learn. Res., 2024)

Fairness Under Demographic Scarce Regime. - Patrik Joslin Kenfack; Samira Ebrahimi Kahou; Ulrich Aïvodji (Trans. Mach. Learn. Res., 2024)

Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy. - Rushabh Solanki; Meghana Bhange; Ulrich Aïvodji; Elliot Creager (CoRR, 2025)

Towards Fair In-Context Learning with Tabular Foundation Models. - Patrik Joslin Kenfack; Samira Ebrahimi Kahou; Ulrich Aïvodji (CoRR, 2025)