用高斯过程在线筛选指令微调数据，让大模型训练从拼数量转向拼质量，值得一读。
arXiv:2606.30077v1 Announce Type: new Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to da…
物理约束MCMC遇上化学先验高斯过程，反应网络自动发现有了新解法。
arXiv:2606.23757v1 Announce Type: cross Abstract: Extracting interpretable governing equations from sparse, noisy chemical time-series data remains di…
数字孪生场景中利用导数信息的高斯过程代理模型，提出流式稀疏Cholesky方法大幅提升计算效率与可扩展性。
arXiv:2511.00366v2 Announce Type: replace-cross Abstract: Digital twins are developed to model the behavior of a specific physical asset (or twin), an…
一篇严谨的差分隐私高斯过程后验采样研究，包含主文8页+25页附录，理论深度与实用分析兼备
arXiv:2606.17995v1 Announce Type: cross Abstract: We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entir…
用高斯过程提升大型视觉语言模型的语义不确定性量化能力，带来更可靠的评估方法。
arXiv:2512.14177v3 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust…
训练后的量子神经网络展现出高斯过程行为，揭示其与经典核方法的深层联系，为量子机器学习理论提供新视角
arXiv:2402.08726v2 Announce Type: replace-cross Abstract: We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gate…
图上的Matérn高斯过程：理论推导与图结构结合的创新方法，为图数据建模提供新视角。
arXiv:2010.15538v4 Announce Type: replace-cross Abstract: Gaussian processes are a versatile framework for learning unknown functions in a manner that…