mlsanityData to 14 Oct 2026

Multivariate Uncertainty Quantification with Tomographic Quantile Forests

arXiv 2512.16383 ↗First posted 18 Dec 2025cs.LG stat.MLcited by 1

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Abstract (arXiv)

Quantifying predictive uncertainty is essential for safe and trustworthy real-world AI deployment. Yet, fully nonparametric estimation of conditional distributions remains challenging for multivariate targets. We propose Tomographic Quantile Forests (TQF), a nonparametric, uncertainty-aware, tree-based regression model for multivariate targets. TQF learns conditional quantiles of directional projections ntopy as functions of the input x and the unit direction n. At inference, it aggregates quantiles across many directions and reconstructs the multivariate conditional distribution by minimizing the sliced Wasserstein distance via an efficient alternating scheme with convex subproblems. Unlike classical directional-quantile approaches that typically produce only convex quantile regions and require training separate models for different directions, TQF covers all directions with a single model without imposing convexity restrictions. We evaluate TQF on synthetic and real-world datasets, and release the source code on GitHub.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 56 of its 131 references that are in the map. First posted on arXiv 18 Dec 2025.

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56 of 131 references.

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  1. 2026Multivariate quantile regression via Kolmogorov-Arnold Networkscited by 0