This paper presents a new approach for synthesizing transformations on tree-structured
data, such as Unix directories and XML documents. We consider a general abstraction for …

We propose a generic categorical framework for learning unknown formal languages of
various types (eg finite or infinite words, weighted and nominal languages). Our approach is …

Ensuring the correctness and reliability of deep neural networks is a challenge. Suitable
formal analysis and verification techniques have yet to be developed. One promising …

Accurate description of program inputs remains a critical challenge in the field of
programming languages. Active learning, as a well-established field, achieves exact …

We address the issue of efficiently automating assume-guarantee reasoning for simulation
conformance between finite state systems and specifications. We focus on a non-circular …

This paper presents (i) an active learning algorithm for visibly pushdown grammars and (ii)
shows its applicability for learning surrogate models of recurrent neural networks (RNNs) …

In this paper, we give an overview on some algorithms for learning automata. Starting with
Biermann's and Angluin's algorithms, we describe some of the extensions catering for …

We develop new algorithms for learning monadic node selection queries in unranked trees
from annotated examples, and apply them to visually interactive Web information extraction …

Automata learning is a technique that has successfully been applied in verification, with the
automaton type varying depending on the application domain. Adaptations of automata …

Standard models of language learning are concerned with weak learning: the learner,
receiving as input only information about the strings in the language, must learn to …