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arXiv Machine Learning By Chih-Duo Hong, Hongjian Jiang, Anthony W. Lin, Oliver Markgraf, Julian Parsert, Tony Tan

Synthesis and Verification of Register Automata over Data Sequences (Technical Report)

Read the original on arXiv Machine Learning →

The paper presents a method for synthesizing and verifying Deterministic Register Automata (DRAs) over ordered data sequences such as time series and sensor streams. It introduces a local‑search synthesis procedure and an exact robustness checker that runs in polynomial time for a fixed number of registers, reducing robustness to a shortest‑path problem. The framework, implemented in RAExc, is applied to learn interpretable surrogate models for neural sequence classifiers, providing statistical guarantees of fidelity and local stability while verifying robustness on benchmark languages and sequentialized MNIST.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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