Papers by Yoan Gutiérrez
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text (P19-1)
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| Challenge: | Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the best combination of them. |
| Approach: | They propose a novel AutoML strategy based on probabilistic grammatical evolution to explore the best combination of parameters and features to use when dealing with the knowledge discovery challenge in Spanish text documents. |
| Outcome: | The proposed strategy achieves state-of-the-art and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. |
Demo Application for the AutoGOAL Framework (2020.coling-demos)
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Suilan Estevez-Velarde, Alejandro Piad-Morffis, Yoan Gutiérrez, Andres Montoyo, Rafael Muñoz-Guillena, Yudivián Almeida Cruz
| Challenge: | AutoGOAL is a framework for automatically finding the best way to solve a given computational task. |
| Approach: | They present a web demo that showcases the main characteristics of the AutoGOAL framework in Python and a graph-based representation for machine learning pipelines. |
| Outcome: | The proposed framework can be applied to Natural Language Processing and structured classification problems. |
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing (2020.coling-main)
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| Challenge: | Existing AutoML systems use heterogeneous techniques to build pipelines that combine techniques and algorithms from different frameworks. |
| Approach: | They propose a system for automatic machine learning that uses heterogeneous techniques. |
| Outcome: | The proposed system is evaluated in diverse machine learning problems and compared with other alternatives. |