CONF Sivaprasad_ICML2020_2020/IDIAP Optimizer Benchmarking Needs to Account for Hyperparameter Tuning Sivaprasad, Prabhu Teja Mai, Florian Vogels, Thijs Jaggi, Martin Fleuret, Francois Benchmarking Hyperparameter optimization optimization EXTERNAL http://publications.idiap.ch/attachments/papers/2020/Sivaprasad_ICML2020_2020.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/Sivaprasad_Idiap-RR-19-2019 Related documents Proceedings of the 37th International Conference on Machine Learning Vienna, Austria 2020 https://icml.cc/Conferences/2020/Schedule?showEvent=6589 URL The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under near-optimal problem-specific hyperparameters, and finding these settings may be prohibitively costly for practitioners. In this work, we argue that a fair assessment of optimizers' performance must take the computational cost of hyperparameter tuning into account, i.e., how easy it is to find good hyperparameter configurations using an automatic hyperparameter search. Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, our results indicate that Adam is the most practical solution, particularly in low-budget scenarios. REPORT Sivaprasad_Idiap-RR-19-2019/IDIAP On the Tunability of Optimizers in Deep Learning Sivaprasad, Prabhu Teja Mai, Florian Vogels, Thijs Jaggi, Martin Fleuret, Francois Benchmarking Hyperparameter optimization optimization EXTERNAL http://publications.idiap.ch/attachments/reports/2019/Sivaprasad_Idiap-RR-19-2019.pdf PUBLIC Idiap-RR-19-2019 2019 Idiap December 2019 Under review at ICLR 2020 https://arxiv.org/abs/1910.11758 URL