Search Results for author:
Found 7 papers, 3 papers with code
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models
Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e. g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e. g. approximating the adequate dosage of a drug for a patient in personalized pharmacology).
Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability
Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens.
E2E-FS: An End-to-End Feature Selection Method for Neural Networks
Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and lasso variants.
Mining Human Mobility Data to Discover Locations and Habits
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry.
A scalable saliency-based Feature selection method with instance level information
Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction.
A Wavefront Marching Method for Solving the Eikonal Equation on Cartesian Grids
This paper presents a new wavefront propagation method for dealing with the classic Eikonal equation.
Unsupervised Trajectory Modelling using Temporal Information via Minimal Paths
This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before.
