Learning To Defer To A Population With Limited Demonstrations

• URL: http://arxiv.org/abs/2510.19351v2
• Date: Thu, 23 Oct 2025 01:52:19 GMT
• Title: Learning To Defer To A Population With Limited Demonstrations
• Authors: Nilesh Ramgolam, Gustavo Carneiro, Hsiang-Ting Chen,
• Abstract summary: This paper addresses the critical data scarcity that hinders the practical deployment of learning to defer (L2D) systems to the population.<n>We introduce a context-aware, semi-supervised framework that uses meta-learning to generate expert-specific embeddings from only a few demonstrations.
• Score: 13.40222956306532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the critical data scarcity that hinders the practical deployment of learning to defer (L2D) systems to the population. We introduce a context-aware, semi-supervised framework that uses meta-learning to generate expert-specific embeddings from only a few demonstrations. We demonstrate the efficacy of a dual-purpose mechanism, where these embeddings are used first to generate a large corpus of pseudo-labels for training, and subsequently to enable on-the-fly adaptation to new experts at test-time. The experiment results on three different datasets confirm that a model trained on these synthetic labels rapidly approaches oracle-level performance, validating the data efficiency of our approach. By resolving a key training bottleneck, this work makes adaptive L2D systems more practical and scalable, paving the way for human-AI collaboration in real-world environments. To facilitate reproducibility and address implementation details not covered in the main text, we provide our source code and training configurations at https://github.com/nil123532/learning-to-defer-to-a-population-with-limited-demonstrations.

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