Scholar
Vincent Plassier
Google Scholar ID: LCk-NbsAAAAJ
Ecole Polytechnique
Citations & Impact
Citations
149
H-index
8
i10-index
3
Publications
14
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- 23/09/2024: Delivered a one-hour presentation at IHES, France; 24-27/05/2024: Presented a paper at AISTAT 2024, Spain; 05/10/2023: Defended doctoral thesis at École Polytechnique, France; 23-29/07/2023: Presented a paper at Hawaii Convention Center; 24-27/04/2023: Presented a paper at AISTAT2023, Spain; 24/04/2023: Paper “Conformal Prediction for Federated Uncertainty Quantification Under Label Shift” accepted for ICML 2023; 20/01/2023: Paper “Federated Averaging Langevin Dynamics: Toward a unified theory of and new algorithms” accepted for AISTATS 2023; 13-17/03/2023: Participated in the workshop on “Statistics, Learning, Simulation, and Image” held in Hyères; 24-28/10/2022: Attended an international workshop at CIRM on Computational methods for unifying multiple statistical analyses (Bayesian Fusion); 24-30/07/2022: Participated in the “Math for Machine Learning Summer School” at Mohammed VI University in Ben Guerir, Morocco, and delivered a talk; 07-11/03/2022: Engaged in the workshop titled “New Challenges in Statistical Learning” held in Font-Romeu; 01/2022: Paper “QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning” accepted for AISTATS 2022; 03/2022: Collaborated with François Portier and Johan Segers on the paper “Risk bounds when learning infinitely many response functions by ordinary linear regression”, accepted for publication in Annales de l’Institut Henri Poincaré; 06/2021: Attended the workshop on “Recent advances in machine learning and uncertainty” at CIRM, Marseille; 05/2021: “DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs” accepted to ICML 2021.
Research Experience
- February 2024 - Present: Quantitative Researcher at Huawei Technologies, Lagrange Center; Prior to February 2024: Ph.D. student at CMAP, École Polytechnque.
Education
- Ph.D.: October 2023 from École Polytechnique, CMAP laboratory, supervised by Eric Moulines and Alain Durmus; Master's degree: Applied Mathematics from Ecole Normale Supérieure Paris-Saclay; Master's degree: Mathematics, Vision and Learning (MVA) from ENS Paris-Saclay.
Background
- Research Interests: Machine learning applications, Distributed/Federated Monte Carlo methods, Uncertainty quantification via Conformal Prediction/Bayesian Fusion, Diffusion models. Currently working as a Quantitative Researcher at Huawei Technologies in the Lagrange Center, focusing on distilling diffusion models.
Miscellany
- Personal interests not mentioned
Co-authors