Fast Algorithm Selection Using Learning Curves

By Jan N. van Rijn, Salisu Abdulrahman, Pavel Brazdil, Joaquin Vanschoren. 2015

87 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “For configurations with similar performance, the training time can be used to prefer faster configurations (van Rijn et al., 2015).”
Survey on Automated Machine Learning (2019, methodology): “For configurations with similar performance, the training time can be used to prefer faster configurations (van Rijn et al., 2015).”
Meta-Learning: A Survey (2018, neutral): “In timeconstrained settings, it has been shown that multi-objective rankings (including training time) converge to near-optimal models much faster [1, 134], and provide a strong baseline for algorithm comparisons [1, 85].”
Hyperparameter Importance Across Datasets (2017, methodology): “en a lot of recent work and progress on hyperparameter optimization, with methods including random search [2], Bayesian optimization [1, 20, 26, 34, 38], evolutionary optimization [29], meta-learning [6, 16, 30, 32, 40, 41] and banditbased methods [23, 28]. © 2018 Copyright held by the owner/author(”
Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML (2016, background): “Alternatively, performance estimates of algorithms on small subsets of the data set can be used [18].”
Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves Using Previous Builds (2017, background): “1 Learning rate step epochs list {[4,8,12,16], [5,10,15], [10]}”
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, methodology): “algorithm selection optimization algorithms selection of algorithms and their hyper-parameters [38], [50], [51], [52], [53]”