no code implementations • 20 Mar 2024 • Jon Vadillo, Roberto Santana, Jose A. Lozano, Marta Kwiatkowska
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications.
no code implementations • 15 Nov 2023 • Onintze Zaballa, Aritz Pérez, Elisa Gómez-Inhiesto, Teresa Acaiturri-Ayesta, Jose A. Lozano
We propose a Markovian generative model of treatments developed to (i) model the irregular time intervals between medical events; (ii) classify treatments into subtypes based on the patient sequence of medical events and the time intervals between them; and (iii) segment treatments into subsequences of disease progression patterns.
1 code implementation • NeurIPS 2023 • Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity.
no code implementations • 2 Feb 2023 • Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting.
no code implementations • 14 Nov 2022 • Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto
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.
1 code implementation • 31 May 2022 • Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification.
1 code implementation • 5 Jul 2021 • 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.
no code implementations • 28 Dec 2020 • 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.
1 code implementation • 5 Nov 2020 • Gorka Kobeaga, María Merino, Jose A. Lozano
We propose a revisited version of the branch-and-cut algorithm for the orienteering problem which includes new contributions in the separation algorithms of inequalities stemming from the cycle problem, in the separation loop, in the variables pricing, and in the calculation of the lower and upper bounds of the problem.
Optimization and Control Data Structures and Algorithms
1 code implementation • 30 Apr 2020 • Gorka Kobeaga, María Merino, Jose A. Lozano
Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination separation problems.
Data Structures and Algorithms Combinatorics 05C38, 90C10, 90C57
1 code implementation • 14 Apr 2020 • 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 can be easily fooled by the addition of imperceptible yet malicious perturbations to natural inputs.
no code implementations • 11 Feb 2020 • Ane Blázquez-García, Angel Conde, Usue Mori, Jose A. Lozano
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.
no code implementations • 11 Oct 2019 • Ibai Roman, Roberto Santana, Alexander Mendiburu, Jose A. Lozano
Gaussian Process is a state-of-the-art technique for regression and classification that heavily relies on a kernel function.
no code implementations • 1 Apr 2019 • Ibai Roman, Alexander Mendiburu, Roberto Santana, Jose A. Lozano
Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
2 code implementations • 25 Mar 2019 • Pablo Rozas Larraondo, Luigi J. Renzullo, Inaki Inza, Jose A. Lozano
Numerical Weather Prediction (NWP) models represent sub-grid processes using parameterizations, which are often complex and a major source of uncertainty in weather forecasting.
Atmospheric and Oceanic Physics 86-08 I.4.9; I.6.6; J.2
1 code implementation • 17 Sep 2018 • Javier Moreno, Daniel Rodriguez, Antonio Nebro, Jose A. Lozano
Many Pareto-based multi-objective evolutionary algorithms require to rank the solutions of the population in each iteration according to the dominance principle, what can become a costly operation particularly in the case of dealing with many-objective optimization problems.
no code implementations • 12 Jun 2018 • Amaia Abanda, Usue Mori, Jose A. Lozano
The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach.
no code implementations • 9 Jan 2018 • Marco Capó, Aritz Pérez, Jose A. Lozano
The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields.
no code implementations • 31 Aug 2016 • Jonathan Ortigosa-Hernández, Iñaki Inza, Jose A. Lozano
We conclude that using unweighted H\"older means with exponent $p \leq 1$ to average the recalls of all the classes produces adequate scores which are capable of determining whether a classifier is competitive.
no code implementations • 10 Dec 2015 • Roberto Santana, Alexander Mendiburu, Jose A. Lozano
NM-landscapes have been recently introduced as a class of tunable rugged models.