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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
Ensemble Feature Selection trains a ridge-regression linear estimator on an ensemble of noisy channels to estimate process infidelity of non-Clifford gates, validated against IRB on IBM hardware with 0.01 precision over 0.02-0.2 infidelity range.
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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.
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Non-Clifford Benchmarking via Ensemble Feature Selection
Ensemble Feature Selection trains a ridge-regression linear estimator on an ensemble of noisy channels to estimate process infidelity of non-Clifford gates, validated against IRB on IBM hardware with 0.01 precision over 0.02-0.2 infidelity range.