Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration
We show that optimisation and calibration can be decoupled for Conformal Prediction while maintaining valid marginal coverage guarantee.
We show that optimisation and calibration can be decoupled for Conformal Prediction while maintaining valid marginal coverage guarantee.
Selected talk speaker and poster presentation, Workshop on Uncertainty in Machine Learning (WUML), Tartu, Estonia.
We present a unified Bayesian decision-theoretic framework for conformal prediction (CP) that integrates Bayesian posterior predictive scores with risk minimization via Bayesian …