Call for papers: ICGI 2023, 16th International Conference on Grammatical Inference
Rabat (Morocco), July 10-13, 2023
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) is 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, on recent advances of Grammatical Inference for Natural Language Processing and Bioinformatics by Cyril Allauzen (Google NY) and Ahmed Elnaggar (TU München), 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.
The 16th edition of ICGI will also partner with the Transformers+RNN: Algorithms to Yield Simple and Interpretable Representations (TAYSIR) competition, an online challenge on extracting simpler models from already trained neural networks. The conference will include a special session organized by TAYSIR on the presentation of the results of the competition with an opportunity for competitors to present their approach.Invited Speakers
Dana Angluin (Yale University)
Cyril Allauzen (Google NY)
Ahmed Elnaggar (TU München)
Will Merrill (NYU)
We welcome three types of papers:
Formal and/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, or 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.
Typical topics of interest include (but are not limited to):
Theoretical aspects of grammatical inference: learning paradigms, learnability results, the 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 computing 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.
Guidelines for authors
Accepted papers will be published within the Proceedings of Machine Learning Research series (http://proceedings.mlr.press/). Submission instructions can be found on the conference website. 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 (https://ctan.org/tex-archive/macros/latex/contrib/jmlr) since it will be the required format for the final published version.
The peer review process is double-blind: we expect submitted papers to be anonymous.Timeline
The 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
François Coste, Inria Rennes, France
Faissal Ouardi, Mohammed V University in Rabat, Morocco
Guillaume Rabusseau, University of Montreal - Mila, Canada
Leonor Becerra, Laboratoire d’Informatique et Systèmes, Aix-Marseille University, France
Johanna Björklund, Umeå University, Sweden
Alexander Clark, University of Gothenburg, Sweden
François Coste, Univ Rennes, Inria, CNRS, IRISA, France
Rémi Eyraud, Université Jean Monnet, France
Henning Fernau, Univ Trier, Germany
Annie Foret, IRISA & University of Rennes 1, France
Robert Frank, Yale University, USA
Matthias Gallé, Naver Labs Europe
Jeffrey Heinz, Stony Brook University, USA
Falk Howar, TU Clausthal / IPSSE, Germany
Jean-Christophe Janodet, University of Evry, France
Adam Jardine, Rutgers University, USA
Tobias Kappé, Open University of the Netherlands & ILLC, University of Amsterdam, The Nederlands
Aurélien Lemay, INRIA, France
Tianyu Li, McGill University, Canada
Damián López, Universitat Politècnica de València, Spain
William Merrill, New York University, USA
Joshua Moerman, Open University of the Netherlands, The Nederlands
Faissal Ouardi, Mohammed V University in Rabat, Morocco
Guillaume Rabusseau, Montreal University – Mila, Canada
Jonathan Rawski, Stony Brook University, USA
Matteo Sammartino, Royal Holloway University of London, University College London, United Kingdom
Ute Schmid, University of Bamberg, Germany
Jose M.Sempere, Universitat Politècnica de València, Spain
Chihiro Shibata, Hosei University, Japan
Olgierd Unold, Wroclaw University of Science and Technology, Poland
Sicco Verwer, Delft University of Technology, The Nederlands
Gail Weiss, Technion – Israel Institute of Technology, Israel
Wojciech Wieczorek, University of Bielsko-Biala, Poland
Ryo Yoshinaka, Tohoku University, Japan
Menno van Zaanen, North West University, South of Africa