A poly-time algorithm achieves O(β)^{o(d)} volume approximation for minimum-volume β-conditioned ellipsoids with O(α/γ) coverage loss, plus a matching hardness result.
Jordan, and Francis Bach
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Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
Tomographic Quantile Forests estimate multivariate conditional distributions nonparametrically by training one model on directional quantiles and reconstructing via sliced Wasserstein minimization.
Multivariate standardized residuals via Mahalanobis distance from a learned local covariance yield asymptotic conditional coverage for conformal prediction under a derived sufficient condition on the data distribution.
Constructs a computable approximating prediction region containing the full-conformal one for multi-task kernel regression in vector-valued RKHS, with theoretical volume bound for known covariance and empirical improvement over split-conformal.
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
citing papers explorer
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Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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Approximate full-conformal multi-task regression with reproducing kernels
Constructs a computable approximating prediction region containing the full-conformal one for multi-task kernel regression in vector-valued RKHS, with theoretical volume bound for known covariance and empirical improvement over split-conformal.
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A Kernel Nonconformity Score for Multivariate Conformal Prediction
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.