Research map / Probabilistic and scientific modeling / Bayesian optimization
Probabilistic Matrix Factorization for Automated Machine Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PMF to transfer information from pipeline results across datasets and select evaluations with an acquisition function | Automated ML pipeline search · 89 OpenML test datasets | average rank | Helped |
| Use PMF for pipeline selection rather than the compared search methods | Automated ML pipeline search · 89 OpenML test datasets | regret | Helped |
| Train PMF with 90% of observation entries missing (ablation) | PMF · 89 OpenML test datasets | balanced accuracy | Mixed |
| Train PMF on a reduced set of prior datasets (ablation) | PMF · 89 OpenML test datasets | average rank | Helped |
| Set the acquisition-function exploration parameter xi to 0 (ablation) | PMF · 89 OpenML test datasets | regret | No clear effect |
| Add pipeline metadata to the model (ablation) | PMF · 89 OpenML test datasets | performance | No clear effect |
| Use a factorized MLP instead of PMF | Automated ML pipeline search · 89 OpenML test datasets | performance | Hurt |
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