116 citations · 340 across the 21 of their papers we have counts for
8 papers · 1 filter
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the…
A Survey on Preserving Fairness Guarantees in Changing Environments
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The lit…
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Jon Vadillo, Roberto Santana, Jose A. Lozano
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpr…
Analysis of Dominant Classes in Universal Adversarial Perturbations
Jon Vadillo, Roberto Santana, Jose A. Lozano
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to effici…
Probabilistic Load Forecasting Based on Adaptive Online Learning
Verónica Álvarez, Santiago Mazuelas, José A. Lozano
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevan…
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A. Lozano
Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models ca…
A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori +1
Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining…
Evolving Gaussian Process kernels from elementary mathematical expressions
Ibai Roman, Roberto Santana, Alexander Mendiburu +1
Choosing the most adequate kernel is crucial in many Machine Learning applications. Gaussian Process is a state-of-the-art technique for regression and classification that heavily…