提出新方法解决神经自回归模型在多维数据中的过拟合问题
Joint Optimization of Neural Autoregressors via Scoring rules
- 基于评分规则联合优化神经自回归模型，避免显式网格构建
- 在低数据场景下参数量显著减少，有效缓解过拟合
- 适合小样本多维分布建模任务，如医疗数据分析
非参数分布回归近年来取得显著进展，其中表格式先验-数据拟合网络（TabPFN）在多个基准测试中表现领先。然而，将这类基于网格的方法推广到真正的多变量场景仍面临挑战。在每维使用 $N$ 个桶的朴素非参数离散化中，显式联合网格的复杂度呈指数级增长，神经网络的参数量也急剧上升。这种扩展性问题在数据稀缺场景下尤为严重，因为最终投影层需要大量参数，导致严重过拟合和计算不可行。
原文摘要 · Abstract (English)
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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