51 citations · 175 across the 32 of their papers we have counts for
33 papers
PAC learning PDFA from data streams
Robert Baumgartner, Sicco Verwer
This is an extended version of our publication Learning state machines from data streams: A generic strategy and an improved heuristic, International Conference on Grammatical Infe…
Automated Test-Case Generation for REST APIs Using Model Inference Search Heuristic
Clinton Cao, Annibale Panichella, Sicco Verwer
The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-ar…
State Frequency Estimation for Anomaly Detection
Clinton Cao, Agathe Blaise, Annibale Panichella +1
Many works have studied the efficacy of state machines for detecting anomalies within NetFlows. These works typically learn a model from unlabeled data and compute anomaly scores f…
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Mathijs M. de Weerdt +2
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally opti…
Optimizing Interpretable Decision Tree Policies for Reinforcement Learning
Daniël Vos, Sicco Verwer
Reinforcement learning techniques leveraging deep learning have made tremendous progress in recent years. However, the complexity of neural networks prevents practitioners from und…
PDFA Distillation via String Probability Queries
Robert Baumgartner, Sicco Verwer
Probabilistic deterministic finite automata (PDFA) are discrete event systems modeling conditional probabilities over languages: Given an already seen sequence of tokens they retur…