# Joint Optimization of Neural Autoregressors via Scoring rules

arXiv: https://arxiv.org/abs/2601.05683 · HTML: https://mlsanity.com/paper/2601.05683/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)

## Built on by (most cited first, 2 of 2)

- [ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules](https://mlsanity.com/paper/2603.29928/) (2026)
- [Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules](https://mlsanity.com/paper/2603.08206/) (2026)

## Abstract (arXiv)

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.