CV

Education

• PhD in Machine Learning (started in October 2024)
  • Institut de Mathématiques de Toulouse and MIND, Inria Paris-Saclay
  • Under the supervision of Pierre Neuvial and Bertrand Thirion
  • ELLIS PhD student
• MSc in Mathematics and Artificial Intelligence (September 2022-October 2024)
  • Institut de Mathématiques d’Orsay & CentraleSupélec, Université Paris-Saclay, 18.43/20
• Double degree in Mathematics and Computer Science (Septembre 2019-Septembre 2022)
  • First and second year in Universidad Complutense de Madrid, 9.09/10
  • Third year in Université Paris-Saclay, 17.9/20

Work experience

• Research Assistant in Statistical Learning (01/04/2024-30/09/2024)
  • Institut de Mathématiques de Toulouse and MIND, Inria Paris-Saclay
  • Under the supervision of Pierre Neuvial and Bertrand Thirion
  • Theoretical study of multiple model-agnostic variable importance measures and the use of the knockoff framework for controlled variable selection.
• Research Assistant in Statistical Learning (01/04/2023-31/07/2023)
  • Laboratoire de Probabilités, Statistique, et Modélisation (LPSM), Sorbonne Université.
  • Under the supervision of Alexis Ayme, Claire Boyer, Erwan Scornet and Aymeric Dieuleveut.
  • Study of the optimality of linear classifiers in the context of prediction with missing data: adaptability to the pattern-by-pattern decomposition.
  • AISTATS 2025 pdf.
  • Internship report pdf.
• Research Assistant in Optimization/ Operational Research (01/06/2022-31/07/2022)
  • Laboratoire Interdisciplinaire des Sciences Numériques, Université Paris-Saclay.
  • Under the supervision of Nicolas Dupin
  • Use of linear programming and insertion algorithms to define neighborhoods and obtain local optimums. Ultimately, perturb and modify neighborhoods to enhance solutions.
  • Published in Roadef 2024 pdf.

Scholarships

• Inspiring Minds Experts Mentoring 2024 (With Mireille Chaleyat-Maurel)
  • Heidelberg Laureate Forum Fundation.
• Excellence scholarship MixtAI 2023
  • Institut DataIA, Université Paris-Saclay.
• Scholarship for young researchers for the 10th HLF (Sept. 2023)
  • Heidelberg Laureate Forum Fundation.
• Excellence scholarship 2019, 2020, 2021 and 2022
  • Science, University and Innovation Council of Madrid.
• Highest Honors with Jury Congratulations(High school) 2019
  • Colegio Valdeluz, Madrid

Publications

Aggregate Models, Not Explanations: Improving Feature Importance Estimation

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.

Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees

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.

A principled approach for comparing Variable Importance

Reyero-Lobo, A., Neuvial, P. , & Thirion, B. (2025). A principled approach for comparing Variable Importance.

When Knockoffs fail: diagnosing and fixing non-exchangeability of Knockoffs

Blain, A., Reyero Lobo, A., Linhart, J., Thirion, B., & Neuvial, P. (2025). When Knockoffs fail: Diagnosing and fixing non-exchangeability of Knockoffs.

Conditional Feature Importance revisited: Double Robustness, Efficiency and Inference

Reyero Lobo, A., Neuvial, P., and Thirion, B. Sobol-cpi: a doubly robust conditional permutation importance statistic. 2025

Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence

Paillard, J., Reyero Lobo, A., Kolodyazhniy, V., Thirion, B., & Engemann, D. A. (2025). Measuring variable importance in heterogeneous treatment effects with confidence.

A primer on linear classification with missing data

Reyero Lobo, A. D., Ayme, A., Boyer, C., and Scornet, E. (2025). A primer on linear classification with missing data.

Analyse statistique d’hill climbers à voisinage large pour le problème d’ordonnancement linéaire

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

Talks

Sobol-CPI: a Doubly Robust Conditional Permutation Importance Statistic

June 05, 2025

Talk at 56th Journées de Statistique de la SFdS, Marseille, France

A primer on classification with missing values

June 04, 2025

Talk at 56th Journées de Statistique de la SFdS, Marseille, France

When knockoffs fail: diagnosing and fixing non-exchangeability of knockoffs

May 20, 2025

Talk at Invited speaker at the 10th Channel Network Conference, Liège, Belgium

A primer on linear classification with missing data

May 04, 2025

Poster at 28th International Conference on Artificial Intelligence and Statistics (AISTATS), Phuket, Thailand

Robust-CPI: A Double Robust Approach to improve Variable Selection.

December 09, 2024

Talk at Workshop on statistical inference for complex data, Alan Turing building, Inria Saclay, France

Inferring variable importance and controlled variable selection

September 30, 2024

Talk at Second year of MSc in Mathematics and AI internship presentation, Institut de Mathémathiques d'Orsay, Université Paris-Saclay, France

Problematic of inferring variable importance with high-dimensional and correlated settings

August 30, 2024

Poster at Poster session, ELLIS Doctoral Symposium 2024, Paris, France

Prediction with missing values and linear classifiers

July 20, 2023

Talk at First year of MSc in Mathematics and AI internship presentation, Institut de Mathémathiques d'Orsay, Université Paris-Saclay, France

Teaching

Statistics

Linear algebra

Data science

Data Analysis

Advanced Statistical Learning