Neural Networks and Random Forests
Retraining neural networks can recover diversity but at substantially higher wall-clock cost than GP conditioning. Random forest degeneracy shows that merely incorporating new data is insufficient for pseudo-observation batch selection. Re…
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Retraining neural networks can recover diversity but at substantially higher wall-clock cost than GP conditioning. Random forest degeneracy shows that merely incorporating new data is insufficient for pseudo-observation batch selection. Rebuilt random forests remain degenerate because bootstrap sampling dilutes the effect of one pseudo-observation. Non-GP alternatives such as neural networks can cause batch points to collapse to identical locations under pseudo-observation selection.