104 citations · 175 across the 13 of their papers we have counts for
13 papers
From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems
Eduardo C. Garrido-Merchán
A trained deep reinforcement learning policy is a black box, and we ask whether it can be made explainable by rewriting it as an executable logic program that reproduces its behavi…
Bayesian Extreme Value Theory with Hawkes-AR-Gumbel Dependence for Extreme CVaR Estimation in Operational Risk
Juan Ballesteros Gómez, Eduardo C. Garrido-Merchán, Pedro Pablo Pérez-Velasco
Operational risk capital estimation under Basel II/III requires quantifying aggregate losses at extreme confidence levels of 99.9% and beyond, yet the standard Loss Distribution Ap…
Real Customization or Just Marketing: Are Customized Versions of Chat GPT Useful?
Eduardo C. Garrido-Merchán, Jose L. Arroyo-Barrigüete, Francisco Borrás-Pala +4
Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalized t…
From computational ethics to morality: how decision-making algorithms can help us understand the emergence of moral principles, the existence of an optimal behaviour and our ability to discover it
Eduardo C. Garrido-Merchán, Sara Lumbreras-Sancho
This paper adds to the efforts of evolutionary ethics to naturalize morality by providing specific insights derived from a computational ethics view. We propose a stylized model of…
An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications
Eduardo C. Garrido-Merchán, Álvaro López López
Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in modern computer science. This automated sur…
Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks
Eduardo C. Garrido-Merchán, Cristina González-Barthe, María Coronado Vaca
In recent years there has been a growing demand from financial agents, especially from particular and institutional investors, for companies to report on climate-related financial…
Bayesian Optimization of ESG Financial Investments
Eduardo C. Garrido-Merchán, Gabriel González Piris, Maria Coronado Vaca
Financial experts and analysts seek to predict the variability of financial markets. In particular, the correct prediction of this variability ensures investors successful investme…
Optimizing Integrated Information with a Prior Guided Random Search Algorithm
Eduardo C. Garrido-Merchán, Javier Sánchez-Cañizares
Integrated information theory (IIT) is a theoretical framework that provides a quantitative measure to estimate when a physical system is conscious, its degree of consciousness, an…
Do Artificial Intelligence Systems Understand?
Eduardo C. Garrido-Merchán, Carlos Blanco
Are intelligent machines really intelligent? Is the underlying philosophical concept of intelligence satisfactory for describing how the present systems work? Is understanding a ne…
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Florian Karl, Tobias Pielok, Julia Moosbauer +10
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preproc…
A Similarity Measure of Gaussian Process Predictive Distributions
Lucia Asencio-Martín, Eduardo C. Garrido-Merchán
Some scenarios require the computation of a predictive distribution of a new value evaluated on an objective function conditioned on previous observations. We are interested on usi…
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld +18
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensi…
Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints
Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
We present MESMOC+, an improved version of Max-value Entropy search for Multi-Objective Bayesian optimization with Constraints (MESMOC). MESMOC+ can be used to solve constrained mu…