18 citations · 42 across the 7 of their papers we have counts for
7 papers
Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation
Ashraf Ghiye, Baptiste Barreau, Laurent Carlier +1
Graph Neural Networks have significantly advanced research in recommender systems over the past few years. These methods typically capture global interests using aggregated past in…
Robust time series generation via Schrödinger Bridge: a comprehensive evaluation
Alexandre Alouadi, Baptiste Barreau, Laurent Carlier +1
We investigate the generative capabilities of the Schrödinger Bridge (SB) approach for time series. The SB framework formulates time series synthesis as an entropic optimal interpo…
Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
Ashraf Ghiye, Baptiste Barreau, Laurent Carlier +1
Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide rel…
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning
Jeremi Assael, Thibaut Heurtebize, Laurent Carlier +1
As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We pro…
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning
Jérémi Assael, Laurent Carlier, Damien Challet
We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market. Using interpretable machine learnin…
History-Augmented Collaborative Filtering for Financial Recommendations
Baptiste Barreau, Laurent Carlier
In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such enviro…
Deep Prediction of Investor Interest: a Supervised Clustering Approach
Baptiste Barreau, Laurent Carlier, Damien Challet
We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clu…