AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
Breiman, Mach
2 Pith papers cite this work. Polarity classification is still indexing.
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LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.
citing papers explorer
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A Scalable Configuration-Interaction Impurity Solver via Active Learning
AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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The Generalization Gap in Machine Learning EoS Inference from Core-Collapse Supernova Gravitational Waves
LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.