The paper defines robustness radius and contamination need for Bayes acts under prior perturbations via linear programming, then builds cost-adjusted selection paths that transition between stability and cost regimes.
Contributions to the decision theoretic foundations of machine learning and robust statistics under weakly structured information
2 Pith papers cite this work. Polarity classification is still indexing.
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SSLA approximates the posterior predictive distribution by refitting Bayesian models on self-predicted data, providing a sampling-free method that improves predictive calibration over classical Laplace approximations in regression tasks.
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Robust Bayes Acts under Prior Perturbations: Contamination, Stability, and Selection Paths
The paper defines robustness radius and contamination need for Bayes acts under prior perturbations via linear programming, then builds cost-adjusted selection paths that transition between stability and cost regimes.
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Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
SSLA approximates the posterior predictive distribution by refitting Bayesian models on self-predicted data, providing a sampling-free method that improves predictive calibration over classical Laplace approximations in regression tasks.