论文标题
具有结构化输出的有效且可靠的概率互动学习
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs
论文作者
论文摘要
在该职位论文中，我们研究了结构化输出空间的交互式学习，重点是主动学习，其中标签是未知的，必须获得的，并且对持怀疑态度的学习，其中标签嘈杂，可能需要重新标记。这些场景需要表达的模型，以保证对概率数量的可靠，有效计算以衡量不确定性。我们确定了一类概率模型（我们表示薯片）符合所有这些条件的条件，从而在保留表现力的同时可以对上述数量进行易处理的计算。在先前在可处理的概率电路上的工作的基础上，我们说明了薯片如何在较大的结构化输出空间中实现强大而有效的主动和持怀疑态度的学习。
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.
