Tomographic Quantile Forests estimate multivariate conditional distributions nonparametrically by training one model on directional quantiles and reconstructing via sliced Wasserstein minimization.
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects
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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.
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Multivariate Uncertainty Quantification with Tomographic Quantile Forests
Tomographic Quantile Forests estimate multivariate conditional distributions nonparametrically by training one model on directional quantiles and reconstructing via sliced Wasserstein minimization.
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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.