Search Results for author: Miguel Romero

Found 8 papers, 1 papers with code

The Distributional Uncertainty of the SHAP score in Explainable Machine Learning

no code implementations23 Jan 2024 Santiago Cifuentes, Leopoldo Bertossi, Nina Pardal, Sergio Abriola, Maria Vanina Martinez, Miguel Romero

In this paper, we propose a principled framework for reasoning on SHAP scores under unknown entity population distributions.

A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs

no code implementations6 Oct 2023 Tamara Cucumides, Daniel Daza, Pablo Barceló, Michael Cochez, Floris Geerts, Juan L Reutter, Miguel Romero

We introduce a framework for answering arbitrary graph pattern queries over incomplete knowledge graphs, encompassing both cyclic queries and tree-like queries with existentially quantified leaves.

Knowledge Graphs

Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification

no code implementations13 Jul 2022 Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens

The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies.

Hierarchical Multi-label Classification

On Computing Probabilistic Explanations for Decision Trees

no code implementations30 Jun 2022 Marcelo Arenas, Pablo Barceló, Miguel Romero, Bernardo Subercaseaux

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models.

Explainable Artificial Intelligence (XAI)

Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification

1 code implementation25 Mar 2022 Miguel Romero, Oscar Ramírez, Jorge Finke, Camilo Rocha

Gene annotation addresses the problem of predicting unknown associations between gene and functions (e. g., biological processes) of a specific organism.

Clustering Hierarchical Multi-label Classification

A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks

no code implementations23 Mar 2022 Miguel Romero, Jorge Finke, Camilo Rocha

Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network.

Classification Hierarchical Multi-label Classification +1

Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction

no code implementations22 Jun 2020 Miguel Romero, Jorge Finke, Camilo Rocha, Luis Tobón

The spectral evolution model aims to characterize the growth of large networks (i. e., how they evolve as new edges are established) in terms of the eigenvalue decomposition of the adjacency matrices.

Link Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.