Short Bio

Since November 2014, I am a (tenured) researcher at Inria, member of the MODAL project-team (MOdels for Data Analysis and Learning) of the Inria Lille - Nord Europe research centre, France. I am also affiliated with the Laboratoire Paul Painlevé (UMR CNRS 8524), which is the mathematics department of the University of Lille.

I obtained a Ph.D. in mathematics in 2013 from UPMC (Université Pierre & Marie Curie, France) under the supervision of Gérard Biau and Éric Moulines. Prior to that, I was a research assistant at DTU Compute (Denmark) supervised by Gilles Guillot.

News

Research

My main line of research is in statistical machine learning. I am primarily interested in the design, analysis and implementation of statistical learning methods for high dimensional problems. My interests include (but are not limited to): PAC-Bayesian theory, sparsity and high-dimensional statistics, optimisation theory, statistical learning theory, non-negative matrix factorisation, aggregation of estimators and classifiers, MCMC algorithms, (un)supervised learning, online clustering, concentration inequalities...

  • 11 Research Articles
    (5 published)
  • 6 Softwares
    (2 as sole author)
  • 48 Talks since 2010
  • 4 Students
    (2 Ph.D.)
  • 3 Collaborations with companies

Students

  1. Le Li (2014-)
    Ph.D. student co-supervised with Sébastien Loustau • Université d'Angers and iAdvize
  2. Astha Gupta (2016)
    Master student • BITS Pilani
  3. Bhargav Srinivasa Desikhan (2016-2017)
    Master student • BITS Pilani
  4. Arthur Leroy (2017-)
    Ph.D. student co-supervised with Servane Gey and Jean-François Toussaint • INSEP

Publications

arXiv • HAL • Google Scholar • ResearchGate

Preprints

  1. (to be released)
  2. B. Guedj and B. Srinivasa Desikan. Pycobra: A Python Toolbox for Ensemble Learning and Visualisation.
    [ pdf ] • [ link ]
  3. P. Alquier and B. Guedj. Simpler PAC-Bayesian Bounds for Hostile Data.
    [ pdf ] • [ link ]
  4. A. Celisse and B. Guedj. Stability revisited: new generalisation bounds for the Leave-one-Out.
    [ pdf ] • [ link ]
  5. L. Li, B. Guedj and S. Loustau. A Quasi-Bayesian Perspective to Online Clustering.
    [ pdf ] • [ link ] • [ software ]
  6. B. Guedj and S. Robbiano. PAC-Bayesian High Dimensional Bipartite Ranking.
    [ pdf ] • [ link ]

International peer-reviewed journals

  1. P. Alquier and B. Guedj (2017). An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization.
    Mathematical Methods of Statistics, vol. 26(1), 55-67.
    [ pdf ] • [ link ] • [ journal website ]• [ software ]
  2. G. Biau, A. Fischer, B. Guedj and J. D. Malley (2016). COBRA: A Combined Regression Strategy.
    Journal of Multivariate Analysis, vol. 146, 18-28.
    [ pdf ] • [ supplementary material ] • [ journal website ] • [ software: R package ] • [ software: Python pycobra library ]
  3. N. Chopin, S. Gadat, B. Guedj, A. Guyader and E. Vernet (2015). On some recent advances on high dimensional Bayesian statistics.
    ESAIM: Proceedings & Surveys, vol. 51, 293-319.
    [ pdf ] • [ journal website ]
  4. B. Guedj and P. Alquier (2013). PAC-Bayesian Estimation and Prediction in Sparse Additive Models.
    Electronic Journal of Statistics, vol. 7, 264-291.
    [ pdf ] • [ journal website ] • [ software ]
  5. B. Guedj and G. Guillot (2011). Estimating the Location and Shape of Hybrid Zones.
    Molecular Ecology Resources, vol. 11(6), 1119-1123.
    [ pdf ] • [ journal website ] • [ software ]

Academic publications

  1. B. Guedj (2013). Agrégation d'estimateurs et de classificateurs : théorie et méthodes. Ph.D. thesis, UPMC.
    [ pdf ] • [ link ]