根据任务损失设计数据采集策略，提升模型预测性能。
Loss-Driven Bayesian Active Learning
- 基于任意损失函数构建最优选数据目标
- 在回归与分类中均降低测试损失
- 适用于多种损失场景，实用性强
主动学习的核心目标是获取能最大化下游预测性能的数据，但现有方法在针对不同下游任务和损失定制数据采集方面灵活性不足。本文提出一种严格的损失驱动贝叶斯主动学习方法，使数据采集直接针对特定决策问题的损失进行优化。我们证明，任何损失函数均可导出唯一的数据采集目标。关键的是，当损失为加权Bregman散度形式时，其对应目标中的核心成分可解析计算，从而实现实际应用。在回归与分类实验中，使用多种损失函数验证，该方法相比现有技术显著降低测试损失。
原文摘要 · Abstract (English)
The central goal of active learning is to gather data that maximises downstream predictive performance, but popular approaches have limited flexibility in customising this data acquisition to different downstream problems and losses. We propose a rigorous loss-driven approach to Bayesian active learning that allows data acquisition to directly target the loss associated with a given decision problem. In particular, we show how any loss can be used to derive a unique objective for optimal data acquisition. Critically, we then show that any loss taking the form of a weighted Bregman divergence permits analytic computation of a central component of its corresponding objective, making the approach applicable in practice. In regression and classification experiments with a range of different losses, we find our approach reduces test losses relative to existing techniques.
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