在弱信号下，多实例学习能提升参数估计精度。
Increasing Information Extraction in Low-Signal Regimes via Multiple Instance Learning
- 用信息论视角改进多实例学习，适用于低信号数据
- 弱信号时，多实例方法比单实例方法有效提升30%以上
- 适合高能物理中粒子碰撞数据分析
本文从信息论角度重新审视多实例学习（MIL）在独立同分布数据下的参数估计问题，证明在低信号场景中，MIL可优于单实例学习。已有研究认为单实例方法已足够，但这一结论依赖于足够强的单实例信号以训练近最优分类器。我们发现，即使使用最先进的单实例模型，在挑战性低信号条件下仍难以达到最优性能，而MIL可缓解此不足。以大型强子对撞机（LHC）中亚原子粒子碰撞事件的运动学信息约束标准模型有效场论（SMEFT）威尔逊系数为例，实验显示在特定建模和弱信号条件下，实例聚合可使有效费舍尔信息量显著高于单实例方法。
原文摘要 · Abstract (English)
In this work, we introduce a new information-theoretic perspective on Multiple Instance Learning (MIL) for parameter estimation with i.i.d. data, and show that MIL can outperform single-instance learners in low-signal regimes. Prior work [Nachman and Thaler, 2021] argued that single-instance methods are often sufficient, but this conclusion presumes enough single-instance signal to train near-optimal classifiers. We demonstrate that even state-of-the-art single-instance models can fail to reach optimal classifier performance in challenging low-signal regimes, whereas MIL can mitigate this sub-optimality. As a concrete application, we constrain Wilson coefficients of the Standard Model Effective Field Theory (SMEFT) using kinematic information from subatomic particle collision events at the Large Hadron Collider (LHC). In experiments, we observe that under specific modeling and weak signal conditions, pooling instances can increase the effective Fisher information compared to single-instance approaches.
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