In this position paper, we study interactive learning for structured output
spaces, with a focus on active learning, in which labels are unknown and must
be acquired, and on skeptical learning, in which the labels are noisy and may
need relabeling. These scenarios require expressive models that guarantee
reliable and efficient computation of probabilistic quantities to measure
uncertainty. We identify conditions under which a class of probabilistic models
-- which we denote CRISPs -- meet all of these conditions, thus delivering
tractable computation of the above quantities while preserving expressiveness.
Building on prior work on tractable probabilistic circuits, we illustrate how
CRISPs enable robust and efficient active and skeptical learning in large
structured output spaces.

本文研究基于交互式的学习方式，探讨以 active learning 和 skeptical learning 为主要方向的标签未知和标签噪音的情况下，采用确保可靠和高效运算的具有表现力的概率模型来衡量不确定性，在此条件下，我们研究认证了一类概率模型 CRISPs，以确保保持表现力的同时，可以实现上述量的可计算性。此外，我们展示了如何利用 CRISPs 在大型结构输出空间中实现鲁棒和高效的 active learning 和 skeptical learning。