mlsanityData to 14 Oct 2026

Tsetlin Machine for Solving Contextual Bandit Problems

arXiv 2202.01914 ↗First posted 4 Feb 2022cs.LG cs.AI cs.NEcited by 10

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Abstract (arXiv)

This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

9 of 37 references.

References · 9

  1. 2018The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logiccited by 72
  2. 2018A Contextual Bandit Bake-offcited by 64
  3. 2014A Neural Networks Committee for the Contextual Bandit Problemcited by 23
  4. 2020On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operatorscited by 23
  5. 2020Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scalingcited by 20
  6. 2020Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretabilitycited by 16
  7. 2015Random Forest for the Contextual Bandit Problem - extended versioncited by 11
  8. 2021Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machinecited by 7
  9. 2020Contextual Bandits for adapting to changing User preferences over timecited by 3

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 0; 2025: 6.

10 papers.

  1. 2023Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Sizecited by 14
  2. 2023Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identificationcited by 3
  3. 2025Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clausescited by 2
  4. 2025The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphscited by 2
  5. 2023Contracting Tsetlin Machine with Absorbing Automatacited by 1
  6. 2025Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Spacecited by 1
  7. 2025Uncertainty Quantification in the Tsetlin Machinecited by 1
  8. 2023Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learningcited by 0
  9. 2025A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machinecited by 0
  10. 2025Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Spacecited by 0