An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers U Garciarena, R Santana Expert Systems with Applications 89, 52-65, 2017 | 41 | 2017 |
Evolved GANs for generating Pareto set approximations U Garciarena, R Santana, A Mendiburu Proceedings of the Genetic and Evolutionary Computation Conference, 434-441, 2018 | 15 | 2018 |
Expanding variational autoencoders for learning and exploiting latent representations in search distributions U Garciarena, R Santana, A Mendiburu Proceedings of the Genetic and Evolutionary Computation Conference, 849-856, 2018 | 12 | 2018 |
Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions U Garciarena, R Santana Proceedings of the 2016 on Genetic and Evolutionary Computation Conference …, 2016 | 11 | 2016 |
An investigation of imputation methods for discrete databases and multi-variate time series U Garciarena Masters thesis, University of the Basque Country, 2016. http://hdl. handle …, 2016 | 6 | 2016 |
Evolving imputation strategies for missing data in classification problems with TPOT U Garciarena, R Santana, A Mendiburu arXiv preprint arXiv:1706.01120, 2017 | 5 | 2017 |
Analysis of the Complexity of the Automatic Pipeline Generation Problem U Garciarena, R Santana, A Mendiburu 2018 IEEE Congress on Evolutionary Computation (CEC), 1841-1848, 2018 | 4 | 2018 |
Towards automatic construction of multi-network models for heterogeneous multi-task learning U Garciarena, A Mendiburu, R Santana arXiv preprint arXiv:1903.09171, 2019 | 2 | 2019 |
Towards a more efficient representation of imputation operators in TPOT U Garciarena, A Mendiburu, R Santana arXiv preprint arXiv:1801.04407, 2018 | 2 | 2018 |
Analysis of the transferability and robustness of GANs evolved for Pareto set approximations U Garciarena, A Mendiburu, R Santana Neural Networks 132, 281-296, 2020 | 1 | 2020 |
Automatic Structural Search for Multi-task Learning VALPs U Garciarena, A Mendiburu, R Santana International Conference on Optimization and Learning, 25-36, 2020 | 1 | 2020 |
EvoFlow: A Python library for evolving deep neural network architectures in tensorflow U Garciarena, R Santana, A Mendiburu 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2288-2295, 2020 | | 2020 |
Envisioning the Benefits of Back-Drive in Evolutionary Algorithms U Garciarena, A Mendiburu, R Santana 2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020 | | 2020 |
An investigation of imputation methods for discrete databases and multi-variate time series U Garciarena Hualde | | 2016 |
Prototipo para la integración de datos públicos U Garciarena Hualde | | 2015 |