Guillaume Braun | Riken AIP

About Me

Since April 2023, I am a postdoctoral researcher in the Imperfect Information Learning Team at RIKEN, under the supervision of Masashi Sugiyama, working on learning problems involving partial and noisy labels.

Before that, I was a Ph.D. student at Inria Lille - Nord Europe, part of 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

Strong Consistency Guarantees for Clustering High-Dimensional Bipartite Graphs with the Spectral Method
G. Braun [ArXiv]
Minimax Optimal Clustering of Bipartite Graphs with a Generalized Power Method
G. Braun and H. Tyagi, Information and Inference: A Journal of the IMA (to appear). [ArXiv]
Seeded graph matching for the correlated Wigner model via the projected power method
E. Araya, G. Braun and H. Tyagi [ArXiv]
An iterative clustering algorithm for the Contextual StochasticBlock Model with optimality guarantees
G. Braun, H. Tyagi, and C. Biernacki, 39th International Conference on Machine Learning (ICML), 2022, 2257-2291. [ArXiv]
Clustering multilayer graphs with missing nodes
G. Braun, H. Tyagi, and C. Biernacki, 24th International Conference on Artificial Intelligence and Statistics, 2021, 2260-2268. [PMLR] [Code][Poster]

Talks

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.
Journals: Journal of Machine Learning Research.