A boosting-enhanced Bayesian conjugate model for oncology demand forecasting outperforms ARIMA, LSTM, and XGBoost in trend direction accuracy by up to 38.25% on real Brazilian hospital data.
Machine learning: a probabilistic perspective
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Uncertainty-aware fine-tuning with a decision-theory-based loss produces better-calibrated uncertainty estimates than standard training on free-form QA tasks.
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Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models
A boosting-enhanced Bayesian conjugate model for oncology demand forecasting outperforms ARIMA, LSTM, and XGBoost in trend direction accuracy by up to 38.25% on real Brazilian hospital data.
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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
Uncertainty-aware fine-tuning with a decision-theory-based loss produces better-calibrated uncertainty estimates than standard training on free-form QA tasks.