arXiv cs.AI· Fabian Denoodt, Sibylle Hess·· 9 天前AI 评分23
贝叶斯神经网络何时采样足够？带统计保证的自适应推理时间
When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees
AI 导读
研究提出用置信序列动态决定贝叶斯神经网络每个输入所需的 Monte Carlo 采样数，在保持统计保证的前提下按需分配计算预算。该方法覆盖最可能类别识别、完整预测分布近似和概率阈值决策三类场景，一旦能以目标保证做出决策即停止采样。实验显示其将更多样本分配给模糊输入、更少给简单输入，在保持可靠决策的同时降低整体延迟。
正文 · 原文
Abstract:Bayesian neural network predictions are commonly approximated using a fixed number of Monte Carlo samples per input, without controlling the resulting error that comes from this finite sample. We propose the use of confidence sequences to dynamically determine how many samples are needed while maintaining statistical guarantees. We consider several ways in which predictive probabilities are used, including identifying the most likely class, approximating the full predictive distribution, and resolving probability-threshold decisions. Sampling stops once the corresponding decision can be made with the desired guarantee. Experiments show that the method allocates the computational budget efficiently, assigning more samples to ambiguous inputs than to easy inputs while preserving reliable decisions and reducing overall latency relative to a fixed Monte Carlo budget.
| Comments: | 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: E-Values: From Statistics to ML |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12212 [cs.AI] |
| (or arXiv:2610.12212v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12212 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fabian Denoodt [view email]
[v1]
Thu, 8 Oct 2026 16:01:08 UTC (204 KB)
来源：arXiv cs.AI · arxiv.org