407 citations · 676 across the 25 of their papers we have counts for
12 papers · 1 filter
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
Reinforcement Learning has emerged as a strong alternative to solve optimization tasks efficiently. The use of these algorithms highly depends on the feedback signals provided by t…
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
Ibai Laña, Ignacio, Olabarrieta +1
The estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years. Uncertainty estimation provides the user w…
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
Javier Del Ser, Alejandro Barredo-Arrieta, Natalia Díaz-Rodríguez +2
There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models is required when focusing on…
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
In the early stages of human life, babies develop their skills by exploring different scenarios motivated by their inherent satisfaction rather than by extrinsic rewards from the e…
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Thomas Rojat, Raphaël Puget, David Filliat +3
Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback,…
Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives
Javier Del Ser, David Casillas-Perez, Laura Cornejo-Bueno +4
Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, wi…