Clinique des données
17 mars 2025 @ 14 h 00 min – CBGP, Grande salle de réunion du CBGP (Présentiel) La Clinique des données est un service mis en place par l’ISDM afin de porter assistance à la communauté scientifique sur des thématique liées aux données. Ainsi, toute personne ayant une problématique, une question, un bug est la bienvenue lors des permanences de ce service. Vous serez […]
Publishing in management and innovation journalson Corporate Digital Transformation and AI
19 mai 2025 – 20 mai 2025 @ Toute la journée –
Espace Richter – rue Vendémiaire – Montpellier (Salle B001 – Bât. B)
Un workshop consacré à la digitalisation en management, financé par le Pôle sciences sociales de l’Université de Montpellier et le Laboratoire MRM. Nous aurons l’honneur d’accueillir pendant ces deux jours comme invité Wim Vanhaverbeke, rédacteur en chef de Technovation (FNEGE 2, ABS 3), reconnu internationalement pour ses travaux sur la transformation digitale et l’innovation ouverte.
#Management, #LabéliséHallesIA
Engression: extrapolation through the lens of distributional regression
19 mai 2025 @ 14 h 00 min –
Online
Machine Learning in Montpellier, Theory & Practice – Xinwei Shen (ETH Zürich)
Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree ensemble based quantile regression. We propose a neural networkbased distributional regression methodology called ‘engression’. An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. Furthermore, we find that modelling the conditional distribution on training data can constrain the fitted function outside of the training support, which offers a new perspective to the challenging extrapolation problem in nonlinear regression. In particular, for ‘preadditive noise’ models, where noise is added to the covariates before applying a nonlinear transformation, we show that engression can successfully perform extrapolation under some assumptions such as monotonicity, whereas traditional regression approaches such as least-squares or quantile regression fall short under the same assumptions. Our empirical results, from both simulated and real data, validate the effectiveness of the engression method. The software implementations of engression are available in both R and Python.
Machine Learning in Montpellier, Theory & Practice