Risk-sensitive routing of volatility forecasting specialists reduces high-volatility forecast loss by 24% and underprediction loss by 22% versus a rolling-best baseline on six ETFs.
Hierarchical mixtures of experts and the EM algorithm,
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A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
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Risk-Sensitive Specialist Routing for Volatility Forecasting
Risk-sensitive routing of volatility forecasting specialists reduces high-volatility forecast loss by 24% and underprediction loss by 22% versus a rolling-best baseline on six ETFs.
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A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.