RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
arXiv preprint arXiv:1506.02785 , year=
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Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.
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RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
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Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
Proposes projected quantum kernels with misspecified GP bandit algorithms and regret bounds to trade off expressivity against learnability in quantum kernel optimization.