对比深度高斯过程与集成模型的不确定性可靠性，发现集成更抗分布偏移。
Evaluating Uncertainty in Deep Gaussian Processes
- 用σ点近似提升深度高斯过程的校准能力
- 集成模型在分布偏移下性能与校准更稳定
- 适合关注不确定性建模可靠性的研究者
可靠的不确定性估计对现代机器学习至关重要。深度高斯过程（DGPs）和深度σ点过程（DSPPs）通过层次化扩展高斯过程，在贝叶斯框架下提供有前景的不确定性量化方法。然而，其在分布偏移下的实际校准效果与鲁棒性，相较于深度集成等基线方法仍缺乏充分研究。本文在回归（CASP数据集）与分类（ESR数据集）任务上评估这些模型，考察预测性能（MAE、准确率）、校准效果（NLL、ECE），以及在多种合成特征级分布偏移下的鲁棒性。结果表明，DSPPs在分布内具有良好的校准能力，得益于其σ点近似；但相比深度集成，其在分布偏移下的表现明显更脆弱，各项指标均出现显著下降。研究强调集成方法作为稳健基线的重要性，提示尽管深度高斯过程具备优良的分布内校准，其在实际应用中面对分布偏移时的鲁棒性仍需审慎评估。代码已开源以支持复现。
原文摘要 · Abstract (English)
Reliable uncertainty estimates are crucial in modern machine learning. Deep Gaussian Processes (DGPs) and Deep Sigma Point Processes (DSPPs) extend GPs hierarchically, offering promising methods for uncertainty quantification grounded in Bayesian principles. However, their empirical calibration and robustness under distribution shift relative to baselines like Deep Ensembles remain understudied. This work evaluates these models on regression (CASP dataset) and classification (ESR dataset) tasks, assessing predictive performance (MAE, Accu- racy), calibration using Negative Log-Likelihood (NLL) and Expected Calibration Error (ECE), alongside robustness under various synthetic feature-level distribution shifts. Results indicate DSPPs provide strong in-distribution calibration leveraging their sigma point approximations. However, compared to Deep Ensembles, which demonstrated superior robustness in both per- formance and calibration under the tested shifts, the GP-based methods showed vulnerabilities, exhibiting particular sensitivity in the observed metrics. Our findings underscore ensembles as a robust baseline, suggesting that while deep GP methods offer good in-distribution calibration, their practical robustness under distribution shift requires careful evaluation. To facilitate reproducibility, we make our code available at https://github.com/matthjs/xai-gp.
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