June 23rd, Vancouver, Canada
Website: https://learnaut19.github.io
SUBMISSION DEADLINE March 30th
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, spanning the communities that the Logic in Computer
Science (LICS) conference brings together. Historically, there has been
little interaction between the GI and LICS 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. Similarly to how main machine
learning conferences and workshops are organized, all accepted abstracts
will be part of a poster session held during the workshop. Additionally,
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.
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 and recurrent neural networks.
- 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 **
Lise Getoor (UC Santa Cruz)
Prakash Panangaden (McGill University)
Nils Jansen (Radboud University) (to be confirmed)
** 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=learnaut2019
- Submission deadline: March 30th
- Notification of acceptance: April 25th
- Registration: TBD
** Program Committee **
Dana Angluin (Yale University)
Borja Balle (Amazon Research Cambridge)
Leonor Becerra-Bonache (Université de Saint-Etienne)
Alexander Clark (King's College London)
François Denis (Aix-Marseille Université)
Kousha Etessami (University of Edinburgh)
Dana Fisman (Ben-Gurion University)
Matthias Gallé (Naver Labs Europe)
Colin de la Higuera (Nantes University)
Falk Howar (TU Clausthal)
Makoto Kanazawa (Hosei University)
Ariadna Quattoni (Naver Labs Europe)
Alexandra Silva (University College of London)
Frits Vaandrager (Radboud University)
** Organizers **
Remi Eyraud (Aix-Marseille Université)
Tobias Kappé (University College London)
Guillaume Rabusseau (Université de Montréal / Mila)
Matteo Sammartino (University College London)
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