Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation

ArXi:2605.15789v1 Announce Type: new Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely on conservative bounds rather than point estimates. Predictor-level intervals (e.g., from quantile regression, conformal prediction, variance networks, or Bayesian models) generally do not compose: adding two per-variable intervals need not yield a valid interval for their sum or preserve coverage.