*Apologies for eventual multiple receptions*
The 5th workshop on Learning & Automata - satellite of ICALP/LiCS/FSCD 2024 https://learnaut24.github.io/ July 7th 2024, Tallinn Location: Astra building on the campus of Tallinn University, Tallinn,Estonia .
This event will be conducted in person in Tallinn. Registration is mandatory (please find the corresponding links here: https://compose.ioc.ee/icalp2024/#registration). Please note that early-bird registration closes on May 17.
It is our pleasure to inform you about LearnAut 2024, the fifth edition of the workshop, co-located with ICALP, LiCS and FSCD.
Learning models defining recursive computations, like automata and formal grammars, are the core of the field called Grammatical Inference (GI). The expressive power of these models and the complexity of the associated computational problems are major research topics within mathematical logic and computer science. Historically, there has been little interaction between the GI and ICALP communities, though recently some important results started to bridge the gap between both worlds, including applications of learning to formal verification and model checking, and (co-)algebraic formulations of automata and grammar learning algorithms.
The goal of this workshop is to bring together experts on logic who could benefit from grammatical inference tools, and researchers in grammatical inference who could find in logic and verification new fruitful applications for their methods.
The LearnAut workshop will consist of 3 invited talks and 9 contributed talks from researchers whose submitted works were selected after peer reviewing. An important amount of time will be kept for interactions between participants.
** Invited Speakers **
* Bernhard Aichernig, TU Graz, Austria * Martin Berger, University of Sussex, UK * Ryan Cotterell, ETH Zürich, Switzerland
** Selected papers **
* Learning EFSM Models with Registers in Guards, by German Vega, Michael Foster, Roland Groz, Neil Walkinshaw, Catherine Oriat, and Adenilso Simao * Small Test Suites for Active Automata Learning, by Loes Kruger, Sebastian Junges, and Jurriaan Rot * Learning Closed Signal Flow Graphs, by Ekaterina Piotrovskaya, Leo Lobski, and Fabio Zanasi * PDFA Distillation via String Probability Queries, by Robert Baumgartner and Sicco Verwer * Database-assisted automata learning, by Hielke Walinga, Robert Baumgartner, and Sicco Verwer * Output-decomposed Learning of Mealy Machines, by Rick Koenders and Joshua Moerman * Analyzing constrained LLM through PDFA-learning, by Matías Carrasco, Franz Mayr, Sergio Yovine, Johny Kidd, Martín Iturbide, Juan da Silva, and Alejo Garat * A Theoretical Analysis of the Incremental Counting Ability of LSTM in Finite Precision, by Volodimir Mitarchuk and Rémi Eyraud * DFAMiner: Mining minimal separating DFAs from labelled samples, by Daniele Dell'Erba, Yong Li, and Sven Schewe
** Organizers **
Sophie Fortz (King's College London, UK) Franz Mayr (Universidad ORT Uruguay, UY) Joshua Moerman (Open Universiteit, Heerlen, NL) Matteo Sammartino (Royal Holloway, University of London, UK)
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