[ The Types Forum (announcements only),
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*Apologies for eventual multiple receptions*
The 5th workshop on Learning & Automata - satellite of ICALP/LiCS/FSCD 2024
https://urldefense.com/v3/__https://learnaut24.github.io/__;!!IBzWLUs!RFaDVYipMFd7I05hHMciTRGdZPBJIetIANFFpHDqQAeGT1JlPtvUUyMRZQ4fqFlPD9mrOpBcKbXjW9-ayy8DrgDe-H8kN09UNo1q9N9Pehbl$
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://urldefense.com/v3/__https://compose.ioc.ee/icalp2024/*registration__;Iw!!IBzWLUs!RFaDVYipMFd7I05hHMciTRGdZPBJIetIANFFpHDqQAeGT1JlPtvUUyMRZQ4fqFlPD9mrOpBcKbXjW9-ayy8DrgDe-H8kN09UNo1q9ENabQMl$
).
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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