Research

Publications

T. Moins, J. Arbel, S. Girard, & A. Dutfoy. “On the use of a local R-hat to improve MCMC convergence diagnostic” Bayesian Analysis, 2023 (PDF, Online appendix, Code)
T. Moins, J. Arbel, S. Girard, & A. Dutfoy. “Reparameterization of extreme value framework for improved Bayesian workflow” Computational Statistics and Data Analysis, 2023 (PDF, Code)
T. Moins, J. Arbel, A. Dutfoy & S. Girard. Contributed discussion of “Rank-Normalization, Folding, and Localization: An Improved R-hat for Assessing Convergence of MCMC” by Vehtari et al.” Bayesian Analysis, 2021 (PDF)
T. Moins, D. Aloise & S. Blanchard. “RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues”. RecSys, 2020 (Link, PDF, HTML).

EVA 2023, Invited Talk: “Reparameterization of extreme value framework for improved Bayesian workflow” (Link)
Journée AppliBUGS, Invited Talk:”Reparameterization of extreme value framework for improved Bayesian workflow” (Link)
Invited Seminar: MAP5 - Mathématiques Appliquées à Paris 5 : “Reparameterization of extreme value framework for improved Bayesian workflow” (Link)

CMStats 2022, Invited Talk: “On the use of a local R-hat to improve MCMC convergence diagnostic” (Link)
Invited Seminar: Laboratoire de Mathématiques de Besançon (Université de Franche Comté) : “Reparameterization of extreme value framework for improved Bayesian workflow”
Energy Forecasting Innovation Conference, Invited Talk: “On the use of a local R-hat to improve MCMC convergence diagnostic.” (Link)
One World YoungStatS webinar, Invited Talk: “On the use of a local R-hat to improve MCMC convergence diagnostic” (Link, Slides)
JDS 2022, Communication & Contributed Talk: “On the use of a local R-hat to improve MCMC convergence diagnostic” (Link, PDF)
BAYSM 2022, Poster: “On the use of a local R-hat to improve MCMC convergence diagnostic” (Link)
ISBA 2022, Poster: “On the use of a local R-hat to improve MCMC convergence diagnostic” (Link)

CMStats 2021, Invited Talk: “A Bayesian Framework for Poisson Process Characterization of Extremes with Uninformative Prior” (Link)
End-to-end Bayesian learning, Poster: “Improving MCMC convergence diagnostic with a local version of R-hat” (Link, Poster)
BayesComp-ISBA, Participant in the workshop “Improving MCMC convergence diagnostic with a local version of R-hat” (Link, Poster, Video)
ISBA 2021, Contributed Talk: “A Bayesian Framework for Poisson Process Characterization of Extremes with Uninformative Prior” (Link, Slides)
JDS 2021, Communication & Contributed Talk: “On reparameterisations of the Poisson process model for extremes in a Bayesian framework” (Link, PDF)

T. Moins. “Modèle hybride combinant réseau de neurones convolutifs et modèle basé sur le choix pour la recommandation de sièges”. Mémoire de maîtrise recherche, Polytechnique Montréal, 2020 (PDF)
T. Moins, F. Forbes, V. Stoppin-Mellet. “Détection de signaux émis par des protéines lors d’une expérience de localisation de particules uniques” Internship, Inria Grenoble, 2018 (PDF)

Khalil Leachouri (2021). Master student co-advised with Stéphane Girard and Julyan Arbel. “MCMC convergence diagnostic: experimental results on a local version of R-hat”