Publications

(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

Under Review

  • 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.
  • M. Perez, R. Romero, B. Kang, T. De Bie, J. Lijffijt., C. Laclau. SurvNET: A Survival Analysis Framework for Machine Learning on Temporal Networks.
  • C. Laclau, C. Largeron and M. Choudhary. A Survey on Fairness for Machine Learning on Graphs. under review, available on arxiv

Latest Publications

  • 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. To appear at NeurIPS 2024.
  • V. Brault, E. Devijver and C. Laclau. Mixture of segmentation for heterogeneous functional data. To appear in Electronic Journal of Statistics
  • R. Serrano, C. Laclau, B. Jeudy, C. Largeron. Reconstructing the Unseen: GRIOT for Attributed Graph Imputation with Optimal Transport. ECML-PKDD, 2024.
  • F. Torba, C. Gravier, C. Laclau, A. Kammoun, J. Subercaze. A Study on Hierarchical Text Classification as a Seq2seq Task. ECIR, 2024.
  • A. Gourru, C. Laclau, M. Choudhary, and C. Largeron. Variational Perspective on Fair Edge Prediction. IDA, 2024
  • T. Leteno, A. Gourru, C. Laclau, C. Gravier Fair Text Classification with Wasserstein Independence. EMNLP, 2023.
  • J. Tissier, C. Laclau. Understanding the Benefits of Forgetting when Learning on Dynamic Graphs. ECML-PKDD, 2022.
  • A. Burashnikova, M. Clausel, C. Laclau, F. Iutzeler, Y. Maximov, M-R. Amini. Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation. Journal of Artificial Intelligence Research, 2021
  • S. Sidana, M. Trofimov, O. Horodnytskyi, C. Laclau, Y. Maximov, M-R. Amini. User preference and embedding learning with implicit feedback for recommender systems. Data Mining and Knowledge Discovery, 2021
  • N. Vesseron, I. Redko, C. Laclau. Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond. AISTAT, 2021.
  • C. Laclau., I. Redko, M. Choudhary, C. Largeron. All of the Fairness for Edge Prediction with Optimal Transport. AISTAT, 2021.
    [paper][supplementary][code]
  • I. Redko, C. Laclau. On Fair Cost Sharing Games in Machine Learning. AAAI, 2019.
  • C. Laclau and V. Brault. Noise-free Latent Block Model for High Dimensional Data. Data Mining and Knowledge Discovery, 2018
    [paper][code and data]

Other

Student Supervision


PhD Students

  • 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.
    Co-supervised with F. d'Alche-Buc.
  • 2022 ... - Richard Serrano: Machine Learning on Imperfect Graphs.
    Co-supervised with C. Largeron and B. Jeudy.
  • 2022 ... - Thibaud Leteno: Bias and fairness in compressed language models.
    Co-supervised with Christophe Gravier and Antoine Gourru - ANR Diké
  • 2021 ... - Fatos Torba: Hierarchical multi-label document classification in the context of tender responses to complex tenders.
    Co-supervised with Christophe Gravier, CIFRE with AITenders

Former Student

  • PhD (2015/2018) - Sumit Sidana : Recommender Systems for Online Advertising (co-supervised with M.-R. Amini)