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.
Subagging improves regression tree performance, especially with many splits.
SBPMT combines bagging and boosting for improved classification.
Improved DNN estimator with scalable subsampling for efficient inference.
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…
Agents learn and control complex mechanical systems through shared memories.
The paper analyzes the risk of bagging regularized M-estimators under proportional asymptotics.
We introduce a very general method for sparse and large-scale variable selection. The large-scale regression settings is such that both the number of parameters and the number of samples are extremely large. The proposed method is based on careful combination of penalized estimators, each applied to a random projection…