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.

Lien direct