Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs
This work addresses the challenge of reliable and efficient interactive learning in large structured output spaces, but it appears incremental as it builds on prior tractable probabilistic circuits.
The paper tackles the problem of interactive learning for structured output spaces, focusing on active and skeptical learning, by identifying conditions under which CRISPs enable tractable computation of probabilistic quantities for uncertainty measurement, thus delivering efficient and reliable learning.
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.