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One major challenge when learning dynamical models from actual time series data consists in tackling partial data. Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. In this paper, we extend the LFIT framework to learn from transitions between partial states where some variable values are unknown. By modeling the unknown, we achieve an overestimation of the real system regarding both its dynamics and variables interactions. We show through theoretical results the correctness of our approaches and its effectiveness through an experimental evaluation on benchmarks from biological literature.

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Learning from Interpretation Transition with Unknowns

  • 会议论文

  • 发表日期：

    2026年

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  • Tony Ribeiro,
  • Maxime Folschette,
  • Morgan Magnin,
  • Kotaro Okazaki,
  • Kuo-Yen Lo,
  • Antoine Roquilly,
  • Jérémie Poschmann,
  • Katsumi Inoue

摘要

One major challenge when learning dynamical models from actual time series data consists in tackling partial data. Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. In this paper, we extend the LFIT framework to learn from transitions between partial states where some variable values are unknown. By modeling the unknown, we achieve an overestimation of the real system regarding both its dynamics and variables interactions. We show through theoretical results the correctness of our approaches and its effectiveness through an experimental evaluation on benchmarks from biological literature.