通过输入空间变形增强贝叶斯主动学习的探索能力
Observation-dependent Bayesian active learning via input-warped Gaussian processes
- 用可学习的单调变换重塑输入空间，使不确定性评估依赖实际观测值
- 在多个基准上提升样本效率，尤其在非平稳场景下表现显著
- 适合需要高效数据采集的机器学习任务，如实验设计与优化
贝叶斯主动学习依赖对预测不确定性的精确量化来探索未知函数空间。尽管高斯过程代理是此类任务的标准工具，但一个被低估的事实是：其后验方差仅通过超参数受观测输出影响，导致探索对实际测量值不敏感。我们提出通过学习的单调重参数化对输入空间进行变形，注入观测依赖的反馈机制。该方法使决策策略能根据观测变异性扩展或压缩输入空间区域，从而调控基于方差的采集函数行为。我们证明，虽然此类变形可通过边际似然训练，但一种新颖的自监督目标可带来显著更好的性能。所提方法在多个主动学习基准中提升了样本效率，尤其在非平稳性挑战传统方法的场景下表现突出。
原文摘要 · Abstract (English)
Bayesian active learning relies on the precise quantification of predictive uncertainty to explore unknown function landscapes. While Gaussian process surrogates are the standard for such tasks, an underappreciated fact is that their posterior variance depends on the observed outputs only through the hyperparameters, rendering exploration largely insensitive to the actual measurements. We propose to inject observation-dependent feedback by warping the input space with a learned, monotone reparameterization. This mechanism allows the design policy to expand or compress regions of the input space in response to observed variability, thereby shaping the behavior of variance-based acquisition functions. We demonstrate that while such warps can be trained via marginal likelihood, a novel self-supervised objective yields substantially better performance. Our approach improves sample efficiency across a range of active learning benchmarks, particularly in regimes where non-stationarity challenges traditional methods.
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