发表机构
Zunitech, LLC; Institute of Data Science, University of Engineering and Technology, Lahore, Pakistan（祖尼特科技有限责任公司; 拉合尔工程技术大学数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对离群值扭曲高斯过程回归的问题，提出带生成建模的变分离群值鲁棒高斯过程回归方法，经实验验证其预测精度优于多数基线且计算复杂度为立方级。
AI 中文摘要
由于传统高斯过程回归（GPR）采用高斯观测似然，离群值会严重扭曲模型，导致模型学习与预测不准确。为解决此局限，本文提出一种生成式GPR模型，可捕捉观测特有的污染并自适应减轻离群值影响；随后采用变分广义期望最大化流程学习潜变量与GPR模型参数。在含不同污染设置的合成与真实数据集上的实验表明，所提方法在预测精度上与鲁棒GPR基线具有竞争力，且在若干场景中优于后者；此外，该方法与所对比的GPR方法共享立方级计算复杂度。
英文摘要
Outliers can substantially distort Gaussian process regression (GPR) due to its conventional Gaussian observation likelihood, leading to inaccurate model learning and prediction. To address this limitation, this article introduces a generative GPR model that captures observation-specific contamination and adaptively mitigates the influence of outliers. Subsequently, a variational generalized expectation-maximization procedure is used to learn the latent variables and GPR model parameters. Experiments on synthetic and real datasets under different contamination settings demonstrate that the proposed method remains competitive with-and in several cases outperforms-robust GPR baselines in prediction accuracy. Moreover, the proposed method shares the cubic computational scaling of the compared GPR methods.
Comments5 pages, 1 figure
