Joint Optimization of Neural Autoregressors via Scoring rules

• URL: http://arxiv.org/abs/2601.05683v1
• Date: Fri, 09 Jan 2026 10:05:07 GMT
• Title: Joint Optimization of Neural Autoregressors via Scoring rules
• Authors: Jonas Landsgesell,
• Abstract summary: Tabular Prior-Data Fitted Network (TabPFN) has demonstrated state-of-the-art performance on various benchmarks.<n>In a naive non-parametric discretization with $N$ bins per dimension, the complexity of an explicit joint grid scales exponentially.
• Score: 0.7877961820015923
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Non-parametric distributional regression has achieved significant milestones in recent years. Among these, the Tabular Prior-Data Fitted Network (TabPFN) has demonstrated state-of-the-art performance on various benchmarks. However, a challenge remains in extending these grid-based approaches to a truly multivariate setting. In a naive non-parametric discretization with $N$ bins per dimension, the complexity of an explicit joint grid scales exponentially and the paramer count of the neural networks rise sharply. This scaling is particularly detrimental in low-data regimes, as the final projection layer would require many parameters, leading to severe overfitting and intractability.

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