Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs

• URL: http://arxiv.org/abs/2202.08566v1
• Date: Thu, 17 Feb 2022 10:29:32 GMT
• Title: Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs
• Authors: Stefano Teso, Antonio Vergari
• Abstract summary: We study interactive learning for structured output spaces in which labels are unknown and must be acquired. We identify conditions under which a class of probabilistic models -- which we denote CRISPs -- meet all of these conditions. 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.
• Score: 19.61401415890762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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