Charlotte Laclau

(You may also look at my Google Scholar and dblp profiles). I'm also currently updating my Github where you can find some of the code for the following articles and the resources for my courses.

■ Book/Theses   ■ Journal articles   ■ Conference papers   ■ Informal publications

Preprint / Under Review

• L. Marey, T. Viard, C. Laclau . k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods . Available on [arxiv].
• C. Laclau, C. Largeron and M. Choudhary. A Survey on Fairness for Machine Learning on Graphs. Available on [arxiv].
• L. Davy, S. Clémençon, C. Laclau. Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization. Available on [arxiv].

Latest Publications

• C. Laclau, A. Gourru, W. Maxwell. Fairness in Machine Learning: from Theory to Regulation. Springer Nature [link]. Available in Sept. 2026.
• P. Krzakala, G. Melo, C. Lançon, C. Laclau, R. Flamary, E. A. Thévenot, F. d'Alché-Buc. Lightweight Alignment of Unimodal Foundation Models for Metabolite Identification. ICML 2026 3rd Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences.
• M. Perez, R. Romero, J. Lijffijt, C. Laclau . How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs. Accepted at UAI. Available on [arxiv].
• T. Leteno, M. Perrot, C. Laclau, A. Gourru, C. Gravier. Fair Text Classification via Transferable Representations. JMLR 2025.
  [paper]
• P. Krzakala, G. Melo , C. Laclau, F. d'Alché-Buc, R. Flamary. The quest for the GRAph Level autoEncoder (GRALE). NeurIPS 2025.
  [paper][code]
• M. Perez, R. Romero, B. Kang, T. De Bie, J. Lijffijt., C. Laclau. SimHawNet: a Modified Hawkes Process for Temporal Network Simulation. Data Mining and Knowledge Discovery. Available on [arxiv].
• Q. Bouniot, I. Redko, AN. Mallasto, C. Laclau, ..., S. Kaski. From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport. paper. CVPR 2025.
• T. Leteno, I. Proskurina et al. HistoiresMorales: A French Dataset for Assessing Moral Alignment. To appear @NAACL 2025.
• F. Torba, C. Gravier, C. Laclau, A. Kammoun, J. Subercaze. Decoding the Hierarchy: A Hybrid Approach to Hierarchical Multi-Label Text Classification. ECIR 2025.
• P. Krzakala, J. Yang, R. Flamary, F. d'Alché-Buc, C. Laclau, M. Labeau. Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss. NeurIPS 2024.
  [paper][code]
• V. Brault, E. Devijver and C. Laclau. Mixture of segmentation for heterogeneous functional data. Electronic Journal of Statistics, 2024.
  [paper][code]

Student Supervision

PhD Students

• 2025 ... - Romain Therezien: Bias and fairness in statistical learning: applications to facial recognition.
  Co-supervised with Stephan Clemençon.
• 2024 ... - Louise Davy: Utility-Fairness Trade-off in AI.
  Co-supervised with Stephan Clemençon.
• 2024 ... - Lilian Marey: Revisiting Fairness on Graphs.
  Co-supervised with T. Viard and Deezer Research.
• 2023 ... - Paul Krzakala: Optimal transport distance for modeling graph dynamics.
  Co-supervised with F. d'Alche-Buc and Remi Flamary.
• 2023 ... - Mathilde Perez: Fairness in Temporal Graphs.

Postdoc

• 2025 ... - Gayane Taturyan: Fairness Trade-off for Public Recommender Systems

Former Students/Postdocs

• PhD (2022-2026) - Richard Serrano: Machine Learning on Imperfect Graphs.
  Co-supervised with C. Largeron and B. Jeudy.
• PhD (2022-2025) - Thibaud Leteno: Bias and fairness in compressed language models.
  Co-supervised with Christophe Gravier and Antoine Gourru - ANR Diké
• PhD (2021-2025) - Fatos Torba: Hierarchical multi-label document classification in the context of tender responses to complex tenders.
  Co-supervised with Christophe Gravier, CIFRE with AITenders
• Postdoc (2019-2020) - Julien Tissier: Learning Embeddings of Dynamic Graphs
• PhD (2015/2018) - Sumit Sidana : Recommender Systems for Online Advertising (co-supervised with M.-R. Amini)