Guillaume Braun | Riken AIP

About Me

Since April 2024, I have been a postdoctoral researcher in the High-Dimensional Causal Analysis Team at RIKEN, under the supervision of Masaaki Imaizumi, working on theoretical machine learning and high-dimensional statistics.

Before that, I was a postdoctoral researcher in the Imperfect Information Learning Team from 2023 to 2024. Prior to that, I completed my Ph.D. at Inria Lille - Nord Europe, as part of the MODAL team, supervised by Christophe Biernacki and Hemant Tyagi. My thesis focused on clustering and matching problems on graphs with efficient first-order methods.

Publications

Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent
G. Braun, H.Q. Minh and M. Imaizumi, AISTATS (2025)
VEC-SBM: Optimal Community Detection with Vectorial Edge Covariates
G. Braun and M. Sugiyama, AISTATS (2024) [PMLR]
Strong Consistency Guarantees for Clustering High-Dimensional Bipartite Graphs with the Spectral Method
G. Braun, Electronic Journal of Statistics (2024) [EJS]
Minimax Optimal Clustering of Bipartite Graphs with a Generalized Power Method
G. Braun and H. Tyagi, Information and Inference: A Journal of the IMA (2023). [ArXiv]
Seeded graph matching for the correlated Wigner model via the projected power method
E. Araya, G. Braun and H. Tyagi, Journal of Machine Learning Research (2024) [JMLR]
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
G. Braun, H. Tyagi, and C. Biernacki, ICML (2022). [ArXiv]
Clustering multilayer graphs with missing nodes
G. Braun, H. Tyagi, and C. Biernacki, AISTATS (2021). [PMLR] [Code][Poster]

Talks

Clustering de graphes bipartis en grande dimension : limites statistiques et algorithmes efficaces
Journées MAS 2024, Poitiers.
Clustering Bipartite graphs with the Generalized Power Method
SIAM Workshop on Network Science 2022, lightning talk
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
ICML 2022, Poster session and spotlight talk
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
MODAL Seminar, Lille (online), November 30th, 2021
Clustering graphs with side information
SPSR Workshop, Bucharest (online), November 19th, 2021
Clustering multilayer graphs with missing nodes
MODAL Seminar, Lille, November 2020 [Slides]

Reviewing

Conferences: AISTATS 2021, AISTATS 2022, ICML 2022, AISTATS 2023, AISTATS 2024, AISTATS 2025.
Journals: Journal of Machine Learning Research, Annals of Statistics.