Jose A. Lozano | DeepAI

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Featured Co-authors

Jose M. Peña
32 publications
Novi Quadrianto
18 publications
Roberto Santana
18 publications
Aritz Pérez
15 publications
Pedro Larrañaga
14 publications
Santiago Mazuelas
14 publications
Alexander Mendiburu
12 publications
Christian Blum
10 publications
Paula Gordaliza
7 publications
Daniel Rodriguez
6 publications
Jon Vadillo
5 publications

∙ 02/02/2023

Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations

Several recent works encourage the use of a Bayesian framework when asse...

0 Ainhize Barrainkua, et al. ∙

∙ 11/14/2022

A Survey on Preserving Fairness Guarantees in Changing Environments

Human lives are increasingly being affected by the outcomes of automated...

0 Ainhize Barrainkua, et al. ∙

∙ 05/31/2022

Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees

The statistical characteristics of instance-label pairs often change wit...

0 Verónica Álvarez, et al. ∙

∙ 05/25/2022

Transitions from P to NP-hardness: the case of the Linear Ordering Problem

In this paper we evaluate how constructive heuristics degrade when a pro...

0 Anne Elorza, et al. ∙

∙ 07/05/2021

When and How to Fool Explainable Models (and Humans) with Adversarial Examples

Reliable deployment of machine learning models such as neural networks c...

0 Jon Vadillo, et al. ∙

∙ 12/28/2020

Analysis of Dominant Classes in Universal Adversarial Perturbations

The reasons why Deep Neural Networks are susceptible to being fooled by ...

17 Jon Vadillo, et al. ∙

∙ 11/30/2020

Probabilistic Load Forecasting Based on Adaptive Online Learning

Load forecasting is crucial for multiple energy management tasks such as...

0 Verónica Álvarez, et al. ∙

∙ 11/05/2020

A revisited branch-and-cut algorithm for large-scale orienteering problems

The orienteering problem is a route optimization problem which consists ...

0 Gorka Kobeaga, et al. ∙

∙ 04/30/2020

On Solving Cycle Problems with Branch-and-Cut: Extending Shrinking and Exact Subcycle Elimination Separation Algorithms

In this paper, we extend techniques developed in the context of the Trav...

0 Gorka Kobeaga, et al. ∙

∙ 04/14/2020

Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

Despite the remarkable performance and generalization levels of deep lea...

0 Jon Vadillo, et al. ∙

∙ 02/11/2020

A review on outlier/anomaly detection in time series data

Recent advances in technology have brought major breakthroughs in data c...

0 Ane Blázquez-García, et al. ∙

∙ 05/26/2019

Taxonomization of Combinatorial Optimization Problems in Fourier Space

We propose and develop a novel framework for analyzing permutation-based...

0 Anne Elorza, et al. ∙

∙ 09/17/2018

Merge Non-Dominated Sorting Algorithm for Many-Objective Optimization

Many Pareto-based multi-objective evolutionary algorithms require to ran...

0 Javier Moreno, et al. ∙

∙ 06/12/2018

A review on distance based time series classification

Time series classification is an increasing research topic due to the va...

0 Amaia Abanda, et al. ∙

∙ 01/09/2018

An efficient K -means clustering algorithm for massive data

The analysis of continously larger datasets is a task of major importanc...

0 Marco Capó, et al. ∙

∙ 08/31/2016

Towards Competitive Classifiers for Unbalanced Classification Problems: A Study on the Performance Scores

Although a great methodological effort has been invested in proposing co...

0 Jonathan Ortigosa-Hernández, et al. ∙

∙ 12/10/2015

Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions

NM-landscapes have been recently introduced as a class of tunable rugged...

0 Roberto Santana, et al. ∙

∙ 05/22/2014

Mathematical Programming Strategies for Solving the Minimum Common String Partition Problem

The minimum common string partition problem is an NP-hard combinatorial ...

0 Christian Blum, et al. ∙

∙ 01/16/2013

Combinatorial Optimization by Learning and Simulation of Bayesian Networks

This paper shows how the Bayesian network paradigm can be used in order ...

0 Pedro Larrañaga, et al. ∙