Bandit-Based Automated Machine Learning

By Silvia Nunes das Dôres, Carlos Soares, Duncan Dubugras Alcoba Ruiz. 2018

11 citations recorded by the source corpus.

Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis.

Subfields: Artificial Intelligence

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Even though multi-armed bandit learning can also be applied to continuous optimization (Munos, 2014), in the context of AutoML it is only used in a finite setting in combination with other optimization techniques (Hoffman et al., 2014; Efimova et al., 2017; Gustafson, 2018; das Dôres et al., 2018).”
Survey on Automated Machine Learning (2019, methodology): “This makes bandit learning especially useful for selecting values for categorical hyperparameters, e.g selecting an ML algorithm (Hoffman et al., 2014; Efimova et al., 2017; Gustafson, 2018; das Dôres et al., 2018).”