July 4th - Paris, France and virtually
Website: https://learnaut22.github.io
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.
We invite submissions of recent work, including preliminary research,
related to the theme of the workshop. The Program Committee will select a
subset of the abstracts for oral presentation. At least one author of each
accepted abstract is expected to represent it at the workshop (in person,
or virtually).
Note that accepted papers will be made available on the workshop website
but will not be part of formal proceedings (i.e., LearnAut is a
non-archival workshop).
Topics of interest include (but are not limited to):
- Computational complexity of learning problems involving automata and
formal languages.
- Algorithms and frameworks for learning models representing language
classes inside and outside the Chomsky hierarchy, including tree and graph
grammars.
- Learning problems involving models with additional structure, including
numeric weights, inputs/outputs such as transducers, register automata,
timed automata, Markov reward and decision processes, and semi-hidden
Markov models.
- Logical and relational aspects of learning and grammatical inference.
- Theoretical studies of learnable classes of languages/representations.
- Relations between automata or any other models from language theory and
deep learning models for sequential data.
- Active learning of finite state machines and formal languages.
- Methods for estimating probability distributions over strings, trees,
graphs, or any data used as input for symbolic models.
- Applications of learning to formal verification and (statistical) model
checking.
- Metrics and other error measures between automata or formal languages.
** Invited speakers **
Jeffrey Heinz (Stony Brook University)
Ariadna Quattoni (Universitat Politècnica de Catalunya)
** Submission instructions **
Submissions in the form of extended abstracts must be at most 8
single-column pages long at most (plus at most four for bibliography and
possible appendixes) and must be submitted in the JMLR/PMLR format. The
LaTeX style file is available here:
https://ctan.org/tex-archive/macros/latex/contrib/jmlr
We do accept submissions of work recently published or currently under
review.
- Submission url: https://easychair.org/conferences/?conf=learnaut2022
- Submission deadline: March 31st
- Notification of acceptance: April 30th
- Early registration: TBD
** Program Committee **
TBD
** Organizers **
Remi Eyraud (University of Saint-Étienne)
Tobias Kappé (ILLC, University of Amsterdam)
Guillaume Rabusseau (Mila & DIRO, Université de Montréal)
Matteo Sammartino (Royal Holloway, University of London & University
College London)
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