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Found 3 papers, 1 papers with code
Revisiting Bellman Errors for Offline Model Selection
Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings.
A Robust Hypothesis Test for Tree Ensemble Pruning
In this paper we develop and present a novel theoretically justified hypothesis test of split quality for gradient boosted tree ensembles and demonstrate that using this method instead of the common penalty terms leads to a significant reduction in out of sample loss.
Augmenting Molecular Images with Vector Representations as a Featurization Technique for Drug Classification
One of the key steps in building deep learning systems for drug classification and generation is the choice of featurization for the molecules.
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