用不确定性评估选择对称性模型，更可靠。
On Equivariant Model Selection through the Lens of Uncertainty
- 从不确定性视角比较多种模型选择方法
- 贝叶斯证据与预测性能不一致，因复杂度定义有偏差
- 适合关注模型泛化与对称性设计的研究者
等变模型利用对称性先验提升预测性能，但若架构约束错误反而损害性能。尽管已有研究探索学习或放松约束，但如何在预训练的、具有不同对称性偏置的模型间进行选择仍具挑战。本文从不确定性感知的角度审视该任务，对比了频率学派（通过合取预测）、贝叶斯方法（边际似然）和校准度量与朴素误差评估的差异。结果发现，不确定性度量通常与预测性能一致，但贝叶斯模型证据表现不一。这归因于所用最后层拉普拉斯近似中贝叶斯与几何复杂度概念的不匹配，并讨论了可能的修正方案。研究提示：不确定性可成为指导对称性感知模型选择的有效工具。
原文摘要 · Abstract (English)
Equivariant models leverage prior knowledge on symmetries to improve predictive performance, but misspecified architectural constraints can harm it instead. While work has explored learning or relaxing constraints, selecting among pretrained models with varying symmetry biases remains challenging. We examine this model selection task from an uncertainty-aware perspective, comparing frequentist (via Conformal Prediction), Bayesian (via the marginal likelihood), and calibration-based measures to naive error-based evaluation. We find that uncertainty metrics generally align with predictive performance, but Bayesian model evidence does so inconsistently. We attribute this to a mismatch in Bayesian and geometric notions of model complexity for the employed last-layer Laplace approximation, and discuss possible remedies. Our findings point towards the potential of uncertainty in guiding symmetry-aware model selection.
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