A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Subbagging estimation for big data reduces memory usage while maintaining statistical consistency.
FastForest boosts Random Forest speed by 24%.
The paper relaxes the stability condition to boost confidence in generalization for randomized learning algorithms.
New method stabilizes machine learning predictions across random seeds.
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for subagged estimators, both for classification and regressor. General loss functions and class of predictors with both finite and infinite VC-dimension are considered. We slightly generalize the formali…
Stacking is a general approach for combining multiple models toward greater predictive accuracy. It has found various application across different domains, ensuing from its meta-learning nature. Our understanding, nevertheless, on how and why stacking works remains intuitive and lacking in theoretical insight. In this …
New unbiased variance estimator for random forests using Hoeffding decomposition.
Bagging reduces variance in LID estimation by preserving local distribution of NN distances.