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[2602.13362] Nonparametric Distribution Regression Re-calibration
Summary
The paper presents a novel nonparametric algorithm for re-calibrating predictive distributions in regression, addressing the challenge of ensuring accurate uncertainty estimates without restrictive assumptions.
Why It Matters
Accurate uncertainty estimation is crucial in safety-critical applications. This research offers a new method that improves calibration without the limitations of existing approaches, potentially enhancing decision-making in various fields reliant on probabilistic models.
Key Takeaways
- Introduces a nonparametric re-calibration algorithm for regression.
- Addresses limitations of existing calibration methods that rely on parametric assumptions.
- Demonstrates improved performance across various regression benchmarks.
- Focuses on trustworthy uncertainty estimates over mere prediction accuracy.
- Utilizes a novel characteristic kernel for efficient inference.
Statistics > Machine Learning arXiv:2602.13362 (stat) [Submitted on 13 Feb 2026] Title:Nonparametric Distribution Regression Re-calibration Authors:Ádám Jung, Domokos M. Kelen, András A. Benczúr View a PDF of the paper titled Nonparametric Distribution Regression Re-calibration, by \'Ad\'am Jung and 2 other authors View PDF HTML (experimental) Abstract:A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty. Minimizing overall prediction error often encourages models to prioritize informativeness over calibration, producing narrow but overconfident predictions. However, in safety-critical settings, trustworthy uncertainty estimates are often more valuable than narrow intervals. Realizing the problem, several recent works have focused on post-hoc corrections; however, existing methods either rely on weak notions of calibration (such as PIT uniformity) or impose restrictive parametric assumptions on the nature of the error. To address these limitations, we propose a novel nonparametric re-calibration algorithm based on conditional kernel mean embeddings, capable of correcting calibration error without restrictive modeling assumptions. For efficient inference with real-valued targets, we introduce a novel characteristic kernel over distributions that can be evaluated in $\mathcal{O}(n \log n)$ time for empirical distributions of size $n$. We demonstrate that our method consistently outperforms prior r...
