U. Garciarena, J. Vadillo, A. Mendiburu, R. Santana . Adversarial Perturbations for Evolutionary Optimization. International Conference on Machine Learning, Optimization, and Data Science (LOD), pp. 408-422. Grasmere, Lake District, England – UK

Unai Garciarena . Contributions to Neural Architecture Search in Generative and Heterogeneous Multi-task Modeling. Ph.D. Thesis

I. Esnaola-Gonzalez, U. Garciarena, J. Bermúdez . Semantic Technologies Towards Missing Values Imputation. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2021), pp. 191-196. Kuala Lumpur, Malaysia.

U. Garciarena, N. Lourenço, P. Machado, R. Santana, A. Mendiburu . On the exploitation of neuroevolutionary information. Proceedings of the 2018 Genetic and Evolutionary Conference (GECCO-2020), pp. 279-280. Lille, France.

U. Garciarena, A. Mendiburu and R. Santana. Towards Automatic Construction of Multi-Network Models for Heterogeneous Multi-Task Learning. ACM Transactions on Knowledge Discovery from Data. Vol. 15, Numer 2. Article number 33. 2021

U. Garciarena, A. Mendiburu, and R. Santana . EvoFlow: A Python library for evolving deep neural network architectures in tensorflow. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp. 2288-2295. Canberra, Australia. IEEE Press.

U. Garciarena, A. Mendiburu and R. Santana. Analysis of the transferability and robustness of GANs evolved for Pareto set approximations. Neural Networks. Vol. 132. Pp. 281-296. 2020

U. Garciarena, A. Mendiburu, and R. Santana . Envisioning the Benefits of Back-Drive in Evolutionary Algorithms. Proceedings of the 2020 Congress on Evolutionary Computation (CEC-2020). Glasgow, Scotland. IEEE Press.

U. Garciarena, A. Mendiburu, and R. Santana . Automatic Structural Search for Multi-task Learning VALPs. OLA2020 CCIS Springer proceedings. Cádiz, Spain.

U. Garciarena, R. Santana, and A. Mendiburu . Evolved GANs for generating Pareto set approximations. Proceedings of the 2018 Genetic and Evolutionary Conference (GECCO-2018). Kyoto, Japan. P. 434-441.

U. Garciarena, A. Mendiburu, and R. Santana . Variational autoencoder for learning and exploiting latent representations in search distributions. Proceedings of the 2018 Genetic and Evolutionary Conference (GECCO-2018). Kyoto, Japan. P. 849-856.

U. Garciarena, R. Santana, and A. Mendiburu . Analysis of the complexity of the automatic pipeline generation problem. Proceedings of the 2018 Congress on Evolutionary Computation (CEC-2018). Rio de Janeiro, Brazil. IEEE Press. P. 1841-1841.

U. Garciarena, and R. Santana. An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers. Expert Systems and Applications. Vol. 89. Pp. 52-65. 2017

U. Garciarena. An investigation of imputation methods for discrete databases and multi-variate time series.(End of Masters Job, 2016)

U. Garciarena, and R. Santana Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions.. Workshop on the Repair and Optimisation of Software using Computational Search (Genetic Improvement - 2016). Companion proceedings of the 2016 Genetic and Evolutionary Conference (GECCO-2016), Denver, CO., USA. Pp. 1159-1166. 2016.

U. Garciarena. Prototipo para la integración de datos públicos. (End of Degree Work, 2015)