Published in Preprint, 2026
We provide a theoretical analysis of e-values for Conditional Independence Testing under both the null and alternative hypotheses, assuming the Model-X framework.
Recommended citation: Reyero-Lobo, A., Meziu, M., Arias, S. U., and Grünwald, P. (2026). Sequential conditional independence testing with machine learning models.
Download Paper
Published in International Conference on Machine Learning (ICML 2026), 2026
We show that model-level ensembling improves the stability and accuracy of feature-importance estimates for expressive machine-learning models, providing theoretical guarantees and empirical validation on benchmarks and UK Biobank proteomic data.
Recommended citation: Joseph Paillard, Angel Reyero Lobo, Denis A. Engemann, Bertrand Thirion. Aggregate Models, Not Explanations: Improving Feature Importance Estimation. In Proceedings of the International Conference on Machine Learning (ICML), 2026.
Download Paper
Published in International Conference on Machine Learning (ICML 2026), 2026
We propose a conditional independence testing procedure with valid type-I error and FDR control that accommodates any pretrained model and requires no train–test split.
Recommended citation: Angel Reyero-Lobo, Bertrand Thirion, and Pierre Neuvial. Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees. In Proceedings of the International Conference on Machine Learning (ICML), 2026.
Download Paper
Published in Preprint, 2025
The paper introduces an axiomatic framework and a principled approach to create and evaluate variable importance measures (VIMs), ensuring they avoid spurious correlations and enable fair comparisons. It also offers examples to help practitioners choose and estimate suitable VIMs for their goals and data.
Recommended citation: Reyero-Lobo, A., Neuvial, P. , & Thirion, B. (2025). A principled approach for comparing Variable Importance.
Download Paper
Published in Preprint, 2025
This work identifies and addresses failures of the exchangeability assumption in knockoff-based inference by introducing a diagnostic test and proposing a robust alternative construction method.
Recommended citation: Blain, A., Reyero Lobo, A., Linhart, J., Thirion, B., & Neuvial, P. (2025). When Knockoffs fail: Diagnosing and fixing non-exchangeability of Knockoffs.
Download Paper
Published in Preprint, 2025
Theoretical study of CFI, establishing a double-robust estimation property, clarifying its target estimand and bias, and proposing a procedure with valid type-I error control.
Recommended citation: Reyero Lobo, A., Neuvial, P., and Thirion, B. Sobol-cpi: a doubly robust conditional permutation importance statistic. 2025
Download Paper
Published in ICML, 2025
PermuCATE is a new method for assessing variable importance in estimating heterogeneous treatment effects. It improves on existing techniques by reducing variance, making it more reliable—especially in low-data settings like biomedical applications.
Recommended citation: Paillard, J., Reyero Lobo, A., Kolodyazhniy, V., Thirion, B., & Engemann, D. A. (2025). Measuring variable importance in heterogeneous treatment effects with confidence.
Download Paper
Published in AISTATS, 2025
Prediction in classification with missing values using linear classifiers.
Recommended citation: Reyero Lobo, A. D., Ayme, A., Boyer, C., and Scornet, E. (2025). A primer on linear classification with missing data.
Download Paper
Published in ROADEF24, 2024
Use of linear programming and insertion algorithms to define neighborhoods and obtain local optimums. Ultimately, perturb and modify neighborhoods to enhance solutions.
Recommended citation: Angel David Reyero Lobo, Nicolas Dupin. Analyse statistique d’hill climbers à voisinage large pour le problème d’ordonnancement linéaire. ROADEF 2024, Mar 2024, Amiens, France. ffhal-04450707
Download Paper