Delft (the Netherlands), NEW DATES: October 26-28, 2026
Website: https://icgi2026.tudelft.nl
Contact: [email protected]

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 17th edition of ICGI will be held in Delft, the Netherlands.

Types of contributions

We welcome three types of papers:

• Regular papers describe original contributions (theoretical, empirical, conceptual, or a software tool) in the field of grammatical inference. They 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.
• Extended abstract of published works can be submitted to present already published work. Existing tools or applications of grammatical inference can also be presented in this track.
• WIP papers: We also invite abstracts on work in progress. This allows you to present unfinished ideas that may be of interest to the grammatical inference community.

Only the regular papers will be published in the proceedings. The extended abstracts of published work and WIP papers will receive a light review process.

Topics of interest

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 grammars, graph grammars, ….
• Learning probability distributions over strings, trees or graphs, or transductions thereof.
• Research on query learning, active learning, and other interactive learning paradigms.
• 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 computational models, such as artificial neural networks, automata, grammars, Markov models, and their expressiveness through the lens of formal languages and inference.
• Experimental and theoretical analysis of different approaches to grammatical inference, 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.
• Successful applications of grammatical inference to other areas, including, but not limited to, natural language processing, computational linguistics, model checking, software verification, bioinformatics, robotic planning and control, and pattern recognition.

Guidelines for authors

Accepted regular 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 be warned that reviewers may not read after page 12). We strongly encourage to use the JMLR style file for LaTeX (https://ctan.org/tex-archive/macros/latex/contrib/jmlr); this is required for the final published version.

The peer review process is double-blind: we expect submitted papers to be anonymous.

Timeline (all dates are Anywhere on Earth)

• The submission deadline is: May 29, 2026
• Notification of acceptance: July 10, 2026
• Conference: October 26-28, 2026

ICGI Steering Committee

Johanna Björklund (Umeå University); Jeffrey Heinz (Stony Brook University); Adam Jardine (Rutgers University); Franz Mayr (Universidad ORT Uruguay); Joshua Moerman (Open Universiteit); Guillaume Rabusseau (Montreal University & Mila); Chihiro Shibata (Hosei University); Lena Strobl (Umeå University); Ryo Yoshinaka (Tohoku University)

Local Organisers

Sicco Verwer (TU Delft); Joshua Moerman (Open Universiteit)

Program Committee

Adam Jardine; Alexander Clark; Andrea Pferscher; Benedikt Bollig; Bernhard Aichernig; Chihiro Shibata; Dakotah Lambert; Falk Howar; François Coste; Johanna Björklund; Karl Meinke; Matthias Gallé; Maude Lizaire; Ryan Cotterell; Rémi Eyraud; Sergio Yovine; Steffen van Bergerem; Tiago Ferreira; More TBA…

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, etc).

This 17th edition of ICGI will be held in Delft, the Netherlands.

Types of contributions

We welcome three types of papers:

• Formal and/or technical papers describe original contributions (theoretical, emperical, 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.

Topics of interest

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 grammars, graph grammars, etc.
• Learning probability distributions over strings, trees or graphs, or transductions thereof.
• Research on query learning, active learning, and other interactive learning paradigms.
• 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 computational models, such as artificial neural networks, automata, grammars, Markov models, and their expressiveness through the lens of formal languages and inference.
• Experimental and theoretical analysis of different approaches to grammatical inference, 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.
• Successful applications of grammatical inference to other areas, including, but not limited to, natural language processing, computational linguistics, model checking, 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. 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 be warned that reviewers may not read after page 12). We strongly encourage to use the JMLR style file for LaTeX; this is required for the final published version.

The peer review process is double-blind: we expect submitted papers to be anonymous.

Submission link: https://easychair.org/conferences/?conf=icgi26

Timeline (all dates are Anywhere on Earth)

• The deadline for submissions is: December 17, 2025
• Notification of acceptance: February 2, 2026
• Conference: April 13-15, 2026

Program committee

• Adam Jardine
• Alexander Clark
• Benedikt Bollig
• Bernhard Aichernig
• François Coste
• Karl Meinke
• Rémi Eyraud
• Tiago Ferreira
• Dakotah Lambert
• … More TBA …

ICGI Steering Committee

Johanna Björklund (Umeå University)
Jeffrey Heinz (Stony Brook University)
Adam Jardine (Rutgers University)
Franz Mayr (Universidad ORT Uruguay)
Joshua Moerman (Open Universiteit)
Guillaume Rabusseau (Montreal University & Mila)
Chihiro Shibata (Hosei University)
Lena Strobl (Umeå University)
Ryo Yoshinaka (Tohoku University)

Local Organisers

Sicco Verwer (TU Delft)
Joshua Moerman (Open Universiteit)

Learning and Automata (LearnAut) — ICALP 2024 workshop
July 7th – Tallin, Estonia
Website: https://learnaut24.github.io/
Deadline: April 18
Submission portal: https://easychair.org/conferences/?conf=learnaut2024

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 aim 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.

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.

Submission instructions

Submissions in the form of anonymized extended abstracts must be at most 8 single-column pages long (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, currently under review or work-in-progress.

• Submission url: https://easychair.org/conferences/?conf=learnaut2024
• Submission deadline: April 18
• Notification of acceptance: May 13
• Early registration: May 17 (ICALP)
• Workshop: July 7

Programme Committee

TBA

Invited Speakers

TBA

Organizers

Sophie Fortz (King’s College London, UK)
Franz Mayr (Universidad ORT Uruguay, UY)
Joshua Moerman (Open Universiteit, Heerlen, NL)
Matteo Sammartino (Royal Holloway, University of London, UK)

This 16th edition of ICGI will be hosted by the Faculty of Sciences, Mohammed V University in Rabat, Morocco.

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

More information on the dedicated website.

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.

Machine Learning journal: website, list of special issues, call in pdf format, instruction for authors

Declaration of intention to submit: June 15, 2019
Paper submission deadline: July 15, 2019
(select “S.I.: Grammatical Inference (2019)”)

First call for papers

Scope and Background:

The Machine Learning journal invites submissions on Grammatical Inference – the research discipline focusing on machine and computational learning of symbolic languages, at the crossroad of all research fields interested in learning formal models representing sets of symbolic sequences, trees or graphs (Artificial Intelligence, Computational Linguistics, Bioinformatics, Software Engineering, Robotics, Cybersecurity…). This special issue aims at gathering state-of-the-art practical, algorithmic and theoretical new results in Grammatical Inference.

Topics of interest:

We welcome original research papers on all aspects of grammatical inference including, but not limited to:

• Theoretical aspects of Grammatical Inference: learning paradigms, learnability and learning complexity of classes of languages/representations
• Efficient algorithms and novel approaches for learning language classes, representations and distributions, inside or outside the Chomsky hierarchy, on strings, trees or graphs
• Grammatical Inference paired with semantics representations or information, for instance for learning by situated agents or robots
• New problems and successful applications of Grammatical Inference in practice, for tasks such as unsupervised parsing, biological sequence modelling, web information processing, robot navigation, multi-agent adaptation, machine translation, pattern recognition, language acquisition, software engineering, computational linguistics, spam or malware detection, cognitive psychology, etc.
• Theoretical and experimental analysis of different approaches to language induction, including artificial neural networks, statistical methods, symbolic methods, logical and relational methods, information-theoretic approaches, minimum description length, complexity-theoretic approaches, heuristic methods, etc.
• Fairness and transparency of inference, interpretability of learned models and explanation of predictions in Grammatical Inference

Papers which, at the time of submission, have appeared in archived conference proceedings (e.g., in the proceedings of ICGI 2018 or other related conferences) will be considered provided that the submission contains at least 30% of new material (i.e., important additional theoretical or empirical results, extensions of the method, etc.) as compared to the conference version of the paper. Authors of such submissions will be required to enclose an accompanying letter discussing the differences between the conference paper and their MLJ submission and to describe clearly the overlap at the beginning of the journal submission. The decision on whether the 30% difference requirement is met will be made by the guest editors.

Schedule:

June 15, 2019: Title and abstract submission to guest editors
July 15, 2019: Full paper submission to MLJ
October 30, 2019: Acceptance notification
December 1, 2019: Final version
December 20, 2019: Expected publication (online)

Submission instructions:

Resources for journal authors, including templates and style files, as well as frequency asked questions can be found at: Instructions For Authors (https://www.editorialmanager.com/mach/redirectToBanner.aspx?defaultTarge…)

Submissions should be made via the Machine Learning journal website (http://www.editorialmanager.com/mach/default.aspx). When submitting your paper, be sure to specify that the paper is a contribution for the special issue “S.I.: Grammatical Inference (2019)” so that your paper is assigned to the guest editors.

To help the reviewing process, we ask authors to declare to the guest editors their intention to submit by email ([email protected]) with a title and an abstract (150 to 250 words) for their submission before June 15, 2019.

Guest editors:

Olgierd Unold, Wroclaw University of Science and Technology

François Coste, Inria Rennes – Bretagne Atlantique

Colin de la Higuera, University of Nantes