mlsanityData to 14 Oct 2026

Towards Efficient Active Learning of PDFA

arXiv 2206.09004 ↗First posted 17 Jun 2022cs.FL cs.AI cs.LGcited by 3

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Abstract (arXiv)

We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 17 Jun 2022.

Builds on

2 of 15 references.

References · 2

  1. 2019Learning Deterministic Weighted Automata with Queries and Counterexamplescited by 22
  2. 2019L*-Based Learning of Markov Decision Processes (Extended Version)cited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025Differentially Private 2D Human Pose Estimationcited by 1
  2. 2024Congruence-based Learning of Probabilistic Deterministic Finite Automatacited by 0
  3. 2026Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communicationcited by 0

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