Research map / Probabilistic and scientific modeling / Bayesian optimization
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| tune SWA hyperparameters with OKG instead of published settings | SWA on VGG-16 · CIFAR-10 | test accuracy: 93.64 -> 93.84 | Helped |
| one-shot KG (OKG) via SAA instead of MOE KG | knowledge gradient · synthetic and HPO benchmarks | wall time: up to 6X | Helped |
| use fast predictive covariance estimates instead of standard posterior inference | qEI batch evaluation · GPyTorch GP | wall time: 10--40X | Helped |
| BoTorch acquisition functions vs GPyOpt, MOE, Dragonfly | Bayesian optimization · Hartmann noisy q=4 and HPO tasks | best suggested value | Helped |
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