多专家模型反而更易欠拟合，新方法通过选可信专家解决此问题。
When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
- 提出PiCCE方法，根据证据自适应选择可靠专家
- 实验证明可显著提升多专家学习的预测性能
- 适合需要信任专家系统的高可靠性场景
学习去延迟（L2D）使分类器能放弃预测并转交专家处理，最近已扩展至多专家设置。本文揭示，多专家L2D比单专家情形更具挑战性：分类器的欠拟合成为固有现象，严重损害预测性能，而单专家情况下仅在特定条件下出现。理论分析表明，这源于内在的专家可辨识性问题——从多样化专家池中学习信任谁，该问题在单专家情况下不存在，导致现有欠拟合缓解方法失效。为此，我们提出基于代理的PiCCE（选自信且正确的专家）方法，通过经验证据自适应识别可靠专家，将多专家L2D转化为类单专家学习问题，从而解决多专家欠拟合。进一步证明了其统计一致性，以及恢复类别概率和专家准确率的能力。在多种设置（包括真实世界专家场景）上的大量实验验证了理论结果，并展示了性能提升。
原文摘要 · Abstract (English)
Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that multi-expert L2D is fundamentally more challenging than the single-expert case. With multiple experts, the classifier's underfitting becomes inherent, which seriously degrades prediction performance, whereas in the single-expert setting it arises only under specific conditions. We theoretically reveal that this stems from an intrinsic expert identifiability issue: learning which expert to trust from a diverse pool, a problem absent in the single-expert case and renders existing underfitting remedies failed. To tackle this issue, we propose PiCCE (Pick the Confident and Correct Expert), a surrogate-based method that adaptively identifies a reliable expert based on empirical evidence. PiCCE effectively reduces multi-expert L2D to a single-expert-like learning problem, thereby resolving multi expert underfitting. We further prove its statistical consistency and ability to recover class probabilities and expert accuracies. Extensive experiments across diverse settings, including real-world expert scenarios, validate our theoretical results and demonstrate improved performance.
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