For any function computable by an optimal decision tree with size s, max depth D_opt and average depth Δ_opt, the greedy heuristic builds an ε-approximating tree of size at most exp(Δ_opt D_opt log(e/ε)) under arbitrary product distributions.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Annotator Policy Models learn safety policies from labeling behavior alone, accurately predicting responses and revealing sources of disagreement like policy ambiguity and value pluralism.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
citing papers explorer
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Decision Tree Learning on Product Spaces
For any function computable by an optimal decision tree with size s, max depth D_opt and average depth Δ_opt, the greedy heuristic builds an ε-approximating tree of size at most exp(Δ_opt D_opt log(e/ε)) under arbitrary product distributions.
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Understanding Annotator Safety Policy with Interpretability
Annotator Policy Models learn safety policies from labeling behavior alone, accurately predicting responses and revealing sources of disagreement like policy ambiguity and value pluralism.
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Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.