RoBO : A Flexible and Robust Bayesian Optimization Framework in Python

By Aaron Klein. 2017

71 citations recorded by the source corpus.

Subfields: Optimization & Training, Neural Architecture Search & AutoML

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Citations in the corpus

BOHB: Robust and Efficient Hyperparameter Optimization at Scale (2018, methodology): “Figure 4 compares BOHB to various BO methods, such as Fabolas (Klein et al., 2017a), multi-task Bayesian optimization (MTBO) (Swersky et al., 2013), GP-BO with expected improvement (Snoek et al., 2012; Klein et al., 2017b), RS and HB. BOHB achieved similar performance as Fabolas and worked slightly…”
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization (2019, background): “…been a recent line of work in hyperparam-eter optimization (HPO) (Snoek et al., 2012; Hutter et al., 2011; Bergstra et al., 2011; Li et al., 2017; Klein et al., 2017a; Falkner et al., 2018) 1 and neural architecture search (NAS) (Baker et al., 2017; Zoph and Le, 2017; Real et al., 2017; Elsken…”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “RoBO RoBO (Klein et al., 2017a) is a generic framework for general purpose Bayesian optimization.”
Survey on Automated Machine Learning (2019, methodology): “This implies that synthetic test functions—especially the Branin function which is used quite often in literature to evaluate CASH algorithms, e.g., (Snoek et al., 2012; Eggensperger et al., 2015; Klein et al., 2017)— are not suited as a benchmark as no significant differences can be observed.”
Practical Hyperparameter Optimization for Deep Learning (2018, methodology): “To obtain good anytime performance, a recent trend in BO is to extend the traditional blackbox setting by exploiting cheaper fidelities of the objective function (Swersky et al., 2014; Klein et al., 2017a; Swersky et al., 2013; Kandasamy et al., 2017).”
BoTorch: Programmable Bayesian Optimization in PyTorch (2019, methodology): “RoBO (Klein et al., 2017) implements a collection of models and acquisition functions, including Bayesian neural nets (Springenberg et al., 2016) and multi-ﬁdelity optimization (Klein et al., 2016).”
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 (2021, background): “, 2019), RBFOpt (Costa and Nannicini, 2018), RoBO (Klein et al., 2017), ProBO (Neiswanger et al.”
Towards Reproducible Neural Architecture and Hyperparameter Search (2018, methodology): “1We used SMAC3 from https://github.com/automl/SMAC3 2We used the implementation from Klein et al. (2017)”