F. Tobar, E. Cazelles, T. de Wolff. Computationally-efficient initialisation of GPs: The generalised variogram method. Transactions on Machine Learning Research Journal, 2023. [arXiv]
E. Cazelles, F. Tobar, J. Fontbona. A novel notion of barycenter for probability distributions based on optimal weak mass transport. Advances in Neural Information Processing Systems (NeurIPS), 2021. [arXiv]
E. Cazelles, A. Robert, F. Tobar. The Wasserstein-Fourier Distance for Stationary Time Series. IEEE Transactions on Signal Processing, Vol. 69, pp. 709-721 (2021). Codes available on Github. [journal][arXiv]
J. Bigot, E. Cazelles, N. Papadakis. Central limit theorems for entropy-regularized optimal transport on finite spaces and statistical applications. Electronic Journal of Statistics, Vol. 13, No. 2, 5120-5150 (2019). Codes available on Github. [journal][arXiv]
J. Bigot, E. Cazelles, N. Papadakis. Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration.Information and Inference: A Journal of the IMA, Vol. 8, Issue 4, 719-755 (2019). [journal][arXiv]
E. Cazelles, V. Seguy, J. Bigot, M. Cuturi, N. Papadakis. Log-PCA versus Geodesic PCA of histograms in the Wasserstein space.SIAM Journal on Scientific Computing, 40(2), 429-456 (2018). Codes available on GitHub. [journal][arXiv]
J. Bigot, E. Cazelles, N. Papadakis. Regularized Barycenters in the Wasserstein space. Proc. of Geometric Science of Information, 83-90 (2017).
On September 21st 2018, I defended my Ph.D. thesis on Statistical properties of barycenters in the Wasserstein space and fast algorithms for optimal transport of measures, under the supervision of Jérémie Bigot and Nicolas Papadakis, within the Institut de Mathématiques de Bordeaux, Université de Bordeaux, and for which I received the 2018 Jacques Neveu Prize. Slides of the presentation.