295 citations · 1.1k across the 17 of their papers we have counts for
20 papers
Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent
Alejandra de la Rica Escudero, Eduardo C. Garrido-Merchan, Maria Coronado-Vaca
Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not…
Best uses of ChatGPT and Generative AI for computer science research
Eduardo C. Garrido-Merchan
Generative Artificial Intelligence (AI), particularly tools like OpenAI's popular ChatGPT, is reshaping the landscape of computer science research. Used wisely, these tools can boo…
Deep Reinforcement Learning for ESG financial portfolio management
Eduardo C. Garrido-Merchán, Sol Mora-Figueroa-Cruz-Guzmán, María Coronado-Vaca
This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on…
Simulating H.P. Lovecraft horror literature with the ChatGPT large language model
Eduardo C. Garrido-Merchán, José Luis Arroyo-Barrigüete, Roberto Gozalo-Brizuela
In this paper, we present a novel approach to simulating H.P. Lovecraft's horror literature using the ChatGPT large language model, specifically the GPT-4 architecture. Our study a…
ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective
Alejo Jose G. Sison, Marco Tulio Daza, Roberto Gozalo-Brizuela +1
This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HC…
ChatGPT is not all you need. A State of the Art Review of large Generative AI models
Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchan
During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to pe…
Many Objective Bayesian Optimization
Lucia Asencio Martín, Eduardo C. Garrido-Merchán
Some real problems require the evaluation of expensive and noisy objective functions. Moreover, the analytical expression of these objective functions may be unknown. These functio…
An Artificial Consciousness Model and its relations with Philosophy of Mind
Eduardo C. Garrido-Merchán, Martin Molina, Francisco M. Mendoza
This work seeks to study the beneficial properties that an autonomous agent can obtain by implementing a cognitive architecture similar to the one of conscious beings. Along this d…
Fuzzy Stochastic Timed Petri Nets for Causal properties representation
Alejandro Sobrino, Eduardo C. Garrido-Merchan, Cristina Puente
Imagery is frequently used to model, represent and communicate knowledge. In particular, graphs are one of the most powerful tools, being able to represent relations between object…
Comparing BERT against traditional machine learning text classification
Santiago González-Carvajal, Eduardo C. Garrido-Merchán
The BERT model has arisen as a popular state-of-the-art machine learning model in the recent years that is able to cope with multiple NLP tasks such as supervised text classificati…
Fake News Detection by means of Uncertainty Weighted Causal Graphs
Eduardo C. Garrido-Merchán, Cristina Puente, Rafael Palacios
Society is experimenting changes in information consumption, as new information channels such as social networks let people share news that do not necessarily be trust worthy. Some…
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Real-world problems often involve the optimization of several objectives under multiple constraints. An example is the hyper-parameter tuning problem of machine learning algorithms…
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes
Eduardo C. Garrido Merchán, Martín Molina
Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep l…
Towards Automatic Bayesian Optimization: A first step involving acquisition functions
Eduardo C. Garrido Merchán, Luis C. Jariego Pérez
Bayesian Optimization is the state of the art technique for the optimization of black boxes, i.e., functions where we do not have access to their analytical expression nor its grad…
Uncertainty Weighted Causal Graphs
Eduardo C. Garrido-Merchán, C. Puente, A. Sobrino +1
Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. From an imaginery point of view, causal graphs are a helpful tool for represe…
Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán +1
It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are comm…
Suggesting Cooking Recipes Through Simulation and Bayesian Optimization
Eduardo C. Garrido-Merchán, Alejandro Albarca-Molina
Cooking typically involves a plethora of decisions about ingredients and tools that need to be chosen in order to write a good cooking recipe. Cooking can be modelled in an optimiz…
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato +2
The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is henc…
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Bayesian Optimization (BO) methods are useful for optimizing functions that are expen- sive to evaluate, lack an analytical expression and whose evaluations can be contaminated by…
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Bayesian optimization (BO) methods are useful for optimizing functions that are expensive to evaluate, lack an analytical expression and whose evaluations can be contaminated by no…