Date : 2023-07-10 => 2023-07-13
Lieu : Rabat (Morocco)
Grammatical
Inference is the
research area at the intersection of Machine
Learning and Formal
Language Theory. Since
1993, the International Conference on Grammatical Inference (ICGI)
has been the meeting place for presenting, discovering, and
discussing the latest research results on the foundations
of learning languages,
from theoretical and algorithmic perspectives to their applications
(natural language or document processing, bioinformatics, model
checking and software verification, program synthesis, robotic
planning and control, intrusion detection…).
This
16th edition of ICGI will be held in-person in Rabat,
the modern capital with deep-rooted history of Morocco located on the
Atlantic Coast. To celebrate the 30th anniversary of the ICGI
conference, the program will include a distinguished lecture by Dana
Angluin.
The program will also include two
invited talks,
a half-day
tutorial at
the beginning of the conference on formal
languages and neural models for learning on sequences
by
Will
Merrill,
as well as oral presentations of accepted papers.
Important
dates
-
Deadline
for submissions is:
March 1, 2023 (anywhere on Earth) -
Notification
of acceptance:
May 15, 2023 -
Camera-ready
copy: June 15,
2023 -
Conference: July
10-13, 2023
Topics
of interest
Typical
topics of interest include (but are not limited to):
-
Theoretical
aspects of grammatical inference: learning paradigms, learnability
results, complexity of learning.
-
Learning
algorithms for language classes inside and outside the Chomsky
hierarchy. Learning tree and graph grammars.
-
Learning
probability distributions over strings, trees or graphs, or
transductions thereof. -
Theoretical
and empirical research on query learning, active learning, and other
interactive learning paradigms. -
Theoretical
and empirical research on methods using or including, but not
limited to, spectral learning, state-merging, distributional
learning, statistical relational learning, statistical inference, or
Bayesian learning -
Theoretical
analysis of neural network models and their expressiveness through
the lens of formal languages. -
Experimental
and theoretical analysis of different approaches to grammar
induction, including artificial neural networks, statistical
methods, symbolic methods, information-theoretic approaches, minimum
description length, complexity-theoretic approaches, heuristic
methods, etc. -
Leveraging
formal language tools, models, and theory to improve the
explainability, interpretability, or verifiability of neural
networks or other black box models. -
Learning
with contextualized data: for instance, Grammatical Inference from
strings or trees paired with semantic representations, or learning
by situated agents and robots. -
Novel
approaches to grammatical inference: induction by DNA or quantum
computing, evolutionary approaches, new representation spaces, etc. -
Successful
applications of grammatical learning to tasks in fields including,
but not limited to, natural language processing and computational
linguistics, model checking and software verification,
bioinformatics, robotic planning and control, and pattern
recognition.
Types
of contributions
We
welcome three types of
papers:
-
Formal
or technical papers describe original contributions
(theoretical, methodological, or conceptual) in the field of
grammatical inference. A technical paper should clearly describe the
situation or problem tackled, the relevant state of the art, the
position or solution suggested, and the benefits of the
contribution. -
Position
papers can describe completely new research positions, approaches,
or open problems. Current limits can be discussed. In all cases,
rigor in the presentation will be required. Such papers must
describe precisely the situation, problem, or challenge addressed,
and demonstrate how current methods, tools, and ways of reasoning,
may be inadequate. -
Tool
papers describing a new tool for grammatical inference. The tool
must be publicly available and the paper has to contain several
use-case studies describing the use of the tool. In addition, the
paper should clearly describe the implemented algorithms, input
parameters and syntax, and the produced output.
Guidelines
for authors
Accepted
papers will be published within the Proceedings
of Machine Learning Research series. The total length of the
paper should not exceed 12 pages on A4-size paper (references and
appendix may exceed this limit but Authors are warned that Reviewers
may not read after page 12). The prospective authors are strongly
recommended to use the JMLR
style file for LaTeX since it will be the required format for the
final published version.
All papers should be
submitted electronically by March 01, 2023; the submission URL
is:
https://www.easychair.org/conferences/?conf=icgi2023
The
peer review process is double-blind: we expect submitted papers to be
anonymous.
Notre site web : www.madics.fr
Suivez-nous sur Tweeter : @GDR_MADICS
Pour vous désabonner de la liste, suivre ce lien.