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| Format: | Preprint |
| Publié: |
2026
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| Accès en ligne: | https://arxiv.org/abs/2601.05683 |
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| _version_ | 1866908754885214208 |
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| author | Landsgesell, Jonas |
| author_facet | Landsgesell, Jonas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05683 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Joint Optimization of Neural Autoregressors via Scoring rules Landsgesell, Jonas Soft Condensed Matter Artificial Intelligence 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. |
| title | Joint Optimization of Neural Autoregressors via Scoring rules |
| topic | Soft Condensed Matter Artificial Intelligence |
| url | https://arxiv.org/abs/2601.05683 |