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.
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Published in Preprint, 2025
Measuring nonparametric efficiently variable importance with valid conditional independence testing using conditional permutation importance.
Recommended citation: Reyero Lobo, A., Neuvial, P., and Thirion, B. Sobol-cpi: a doubly robust conditional permutation importance statistic. 2025
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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.
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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.
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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
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