A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (ICML, 2018)

On Tree-Based Methods for Similarity Learning. - Stéphan Clémençon; Robin Vogel (LOD, 2019)

Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon; Ons Jelassi; Guillaume Papa (ECML/PKDD (2), 2019)

A Multiclass Classification Approach to Label Ranking. - Robin Vogel; Stéphan Clémençon (AISTATS, 2020)

Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling. - Robin Vogel; Mastane Achab; Stéphan Clémençon; Charles Tillier (ESANN, 2020)

Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling. - Robin Vogel; Mastane Achab; Stéphan Clémençon; Charles Tillier (CoRR, 2020)

Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (CoRR, 2020)

Ordonnancement par similarité pour la biométrie : théorie et pratique. (Similarity ranking for biometrics : theory and practice). - Robin Vogel (2020)

Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (AISTATS, 2021)

Visual Recognition with Deep Learning from Biased Image Datasets. - Robin Vogel; Stéphan Clémençon; Pierre Laforgue (CoRR, 2021)