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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Calibrating Data-Driven Predictions for Safety-Critical Systems: Challenges and Solutions
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If you have a question about this talk, please contact nobody. RCLW02 - Calibrating prediction uncertainty : statistics and machine learning perspectives As safety-critical systems—ranging from autonomous transport to industrial control systems—become increasingly data-driven, ensuring reliable probabilistic predictions is a fundamental challenge. Historically, the safety of such systems has been ensured through physics-based modeling, scenario analysis, and conservative engineering design. However, as machine learning (ML) models are increasingly used for predictive decision-making, they introduce additional uncertainties that must be well-calibrated to maintain system reliability. This talk explores the role of probabilistic calibration techniques in improving the trustworthiness of ML-based predictions in safety-critical applications. We will discuss: The challenges of uncalibrated ML models in high-risk environments. Approaches for calibrating ML predictions, from conformal prediction to Bayesian calibration. The role of uncertainty-aware experimental design to reduce uncertainty in safety-critical applications. Hybrid approaches that combine physics-based models with data-driven insights to ensure robustness. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
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