提出多输出鲁棒共轭高斯过程，有效抑制异常值对多变量预测的传播影响。
Multi-Output Robust and Conjugate Gaussian Processes
- 基于共轭框架构建多输出鲁棒模型，同时捕捉变量间相关性
- 在金融与癌症研究中验证，显著提升异常值场景下的预测稳定性
- 适合处理存在多重异常响应变量的多输出回归任务
多输出高斯过程（MOGP）回归能够建模多个相关响应变量之间的依赖关系。与标准高斯过程类似，MOGP对模型误设和异常值敏感，可能导致单个输出的预测失真。当多个响应变量存在异常时，由于输出间的相关性，误差会进一步传播。为此，本文扩展并推广了Altamirano等人（2024）提出的鲁棒共轭高斯过程（RCGP）框架，提出多输出RCGP（MO-RCGP）：一种可证明鲁棒、共轭且联合捕捉输出间相关性的多输出高斯过程。通过在金融和癌症研究中的应用进行充分评估，验证了该方法的有效性。
原文摘要 · Abstract (English)
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes, MOGPs are sensitive to model misspecification and outliers, which can distort predictions within individual outputs. This situation can be further exacerbated by multiple anomalous response variables whose errors propagate due to correlations between outputs. To handle this situation, we extend and generalise the robust and conjugate Gaussian process (RCGP) framework introduced by Altamirano et al. (2024). This results in the multi-output RCGP (MO-RCGP): a provably robust MOGP that is conjugate, and jointly captures correlations across outputs. We thoroughly evaluate our approach through applications in finance and cancer research.
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