A federated RuleFit method using differentially private histograms for consistent cutoffs, local GBDT rule generation, and federated dual averaging for l1-regularized coefficients matches centralized RuleFit performance in simulations and delivers interpretable results on real medical data.
Greedy function approximation: a gradient boosting machine
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An adaptive vulnerability-aware fault tolerance framework for neural networks that employs a GNN predictor to dynamically adjust protection policies, achieving over 95% prediction accuracy and 42.12% average overhead reduction.
Gradient boosted trees trained on nuclear data predict level density parameters for superheavy elements with reported standard deviations from 0.00035 to 0.73.
citing papers explorer
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Federated Rule Ensemble Method in Medical Data
A federated RuleFit method using differentially private histograms for consistent cutoffs, local GBDT rule generation, and federated dual averaging for l1-regularized coefficients matches centralized RuleFit performance in simulations and delivers interpretable results on real medical data.
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Adaptive Soft Error Protection for Neural Network Processing
An adaptive vulnerability-aware fault tolerance framework for neural networks that employs a GNN predictor to dynamically adjust protection policies, achieving over 95% prediction accuracy and 42.12% average overhead reduction.
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Trees and Islands -- Machine learning approach to nuclear physics
Gradient boosted trees trained on nuclear data predict level density parameters for superheavy elements with reported standard deviations from 0.00035 to 0.73.