Accepted regular papers

A Congruence-based Approach to Active Automata Learning from Neural Language Models
Franz Mayr, Sergio Yovine, Matías Carrasco, Federico Pan and Federico Vilensky

A Procedure for Inferring a Minimalist Lexicon from an SMT Model of a Language Acquisition Device
Sagar Indurkhya

Benchmarking State-Merging Algorithms for Learning Regular Languages
Adil Soubki and Jeffrey Heinz

Detecting Changes in Loop Behavior for Active Learning
Bram Verboom and Sicco Verwer

Extending Distributional Learning from Positive Data and Membership Queries
Makoto Kanazawa and Ryo Yoshinaka

fAST: regular expression inference from positive examples using Abstract Syntax Trees
Maxime Raynal, Marc-Olivier Buob and Georges Quénot

Formal and Empirical Studies of Counting Behaviour in ReLU RNNs
Nadine El-Naggar, Andrew Ryzhikov, Laure Daviaud, Pranava Madhyastha and Tillman Weyde

Identification of Substitutable Context-Free Languages over Infinite Alphabets from Positive Data
Yutaro Numaya, Diptarama Hendrian, Ryo Yoshinaka and Ayumi Shinohara

Learning state machines from data streams: A generic strategy and an improved heuristic
Robert Baumgarter and Sicco Verwer

Learning Syntactic Monoids from Samples by extending known Algorithms for learning State Machines
Simon Dieck and Sicco Verwer

Learning Transductions and Alignments with RNN Seq2seq Models
Zhengxiang Wang

Lower Bounds for Active Automata Learning
Loes Kruger, Bharat Garhewal and Frits Vaandrager

String Extension Learning Despite Noisy Intrusions
Katherine Wu and Jeffrey Heinz

Accepted work in progress


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