mlsanityData to 14 Oct 2026

TS-Insight: Visualizing Thompson Sampling for Verification and XAI

arXiv 2507.19898 ↗First posted 26 Jul 2025cs.HC cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 26 Jul 2025.

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References · 5

  1. 2017A Tutorial on Thompson Samplingcited by 433
  2. 2015Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challengescited by 114
  3. 2017Taming Non-stationary Bandits: A Bayesian Approachcited by 32
  4. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  5. 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18

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