Publications

Under review

  • T. Moins, J. Arbel, S. Girard, & A. Dutfoy. “On the use of a local R-hat to improve MCMC convergence diagnostic”, 2022+ (PDF, Online appendix, Code)

Published

  • T. Moins, J. Arbel, S. Girard, & A. Dutfoy. “Reparameterization of extreme value framework for improved Bayesian workflow*Computational Statistics and Data Analysis, to appear, 2023 (PDF, Code)

  • T. Moins, J. Arbel, A. Dutfoy & S. Girard. Contributed iscussion 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).

Presentations

2023

  • EVA 2023, Invited Talk (upcoming, 27/06): “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)

2022

  • 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)

2021

  • 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)

 

Thesis

  • 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 uniquesInternship, Inria Grenoble, 2018 (PDF)

 

Reviewing

  • Statistical Papers, Journal, 2021

 

Supervision

  • 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