Neural Networks and Random Forests
Cross-source consensus on Neural Networks and Random Forests from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Risks & contraindications
Comparisons
Highlighted claims
- Non-GP alternatives such as neural networks can cause batch points to collapse to identical locations under pseudo-observation selection. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Random forest degeneracy shows that merely incorporating new data is insufficient for pseudo-observation batch selection. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Rebuilt random forests remain degenerate because bootstrap sampling dilutes the effect of one pseudo-observation. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
- Retraining neural networks can recover diversity but at substantially higher wall-clock cost than GP conditioning. — Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not