Zobrazeno 1 - 10
of 1 661
pro vyhledávání: '"Lozano, Jose A."'
Most Reinforcement Learning (RL) environments are created by adapting existing physics simulators or video games. However, they usually lack the flexibility required for analyzing specific characteristics of RL methods often relevant to research. Thi
Externí odkaz:
http://arxiv.org/abs/2407.03969
Publikováno v:
Presented at the XI Symposium of Theory and Applications of Data Mining from the XX Conference of the Spanish Association for Artificial Intelligence CAEPIA 2024
This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute t
Externí odkaz:
http://arxiv.org/abs/2406.19066
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications. Promising approaches to overcome such limitations are Prototype-Based Self-Explainable Neu
Externí odkaz:
http://arxiv.org/abs/2403.13740
Autor:
Garrido-Merchán, Eduardo C., Arroyo-Barrigüete, Jose L., Borrás-Pala, Francisco, Escobar-Torres, Leandro, de Ibarreta, Carlos Martínez, Ortiz-Lozano, Jose María, Rua-Vieites, Antonio
Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalized through a fine-tuning process to meet the student demands on every part
Externí odkaz:
http://arxiv.org/abs/2312.03728
Autor:
Zaballa, Onintze, Pérez, Aritz, Gómez-Inhiesto, Elisa, Acaiturri-Ayesta, Teresa, Lozano, Jose A.
Electronic health records contain valuable information for monitoring patients' health trajectories over time. Disease progression models have been developed to understand the underlying patterns and dynamics of diseases using these data as sequences
Externí odkaz:
http://arxiv.org/abs/2311.09369
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. The incremental learning of a growing sequence of tasks holds promise to enable acc
Externí odkaz:
http://arxiv.org/abs/2310.15974
Autor:
Santana, Roberto, Hidalgo-Cenalmor, Ivan, Garciarena, Unai, Mendiburu, Alexander, Lozano, Jose Antonio
In some machine learning applications the availability of labeled instances for supervised classification is limited while unlabeled instances are abundant. Semi-supervised learning algorithms deal with these scenarios and attempt to exploit the info
Externí odkaz:
http://arxiv.org/abs/2303.02801
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 Uncertainty Matters (UM) framework that generalizes a Beta-Binomial a
Externí odkaz:
http://arxiv.org/abs/2302.01079
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 literature of algorithmic fairness has grown considerably over the last d
Externí odkaz:
http://arxiv.org/abs/2211.07530
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate of change by means of a care
Externí odkaz:
http://arxiv.org/abs/2205.15942