TS-Insight让泰勒斯采样算法的决策过程可可视化，提升可解释性与信任度。
TS-Insight: Visualizing Thompson Sampling for Verification and XAI
- 通过多图联动展示每支臂的后验分布、证据计数和采样结果
- 支持对探索与利用动态的实时验证与故障诊断
- 适合需要可解释决策的敏感领域开发者使用
泰勒斯采样（Thompson Sampling, TS）及其变体是用于主动学习中平衡探索与利用的强大多臂赌博机算法。然而，其概率性质常使其成为难以调试和信任的“黑箱”。我们提出TS-Insight，一款专为模型开发者设计的可视化分析工具，旨在揭示基于泰勒斯采样的算法内部决策机制。该工具包含多个图表，可追踪每支臂的后验分布演化、证据计数及采样结果，支持对探索/利用动态的验证、诊断与可解释性分析。本工具旨在增强复杂二元决策场景中的信任度，促进有效调试与部署，尤其适用于需可解释决策的敏感领域。
原文摘要 · Abstract (English)
Thompson Sampling (TS) and its variants are powerful Multi-Armed Bandit algorithms used to balance exploration and exploitation strategies in active learning. Yet, their probabilistic nature often turns them into a "black box", hindering debugging and trust. We introduce TS-Insight, a visual analytics tool explicitly designed to shed light on the internal decision mechanisms of Thompson Sampling-based algorithms, for model developers. It comprises multiple plots, tracing for each arm the evolving posteriors, evidence counts, and sampling outcomes, enabling the verification, diagnosis, and explainability of exploration/exploitation dynamics. This tool aims at fostering trust and facilitating effective debugging and deployment in complex binary decision-making scenarios especially in sensitive domains requiring interpretable decision-making.
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