26-28 Aug 2020
New York NY, U.S.A.
The 15th International Conference on Grammatical Inference August 26-28, 2020 Manhattan, New-York, USA Submission deadline: May 1st, 2020 https://icgi2020.lis-lab.fr *Apologies for eventual multiple receptions* It is our pleasure to inform you about ICGI 2020, the major forum for presentation and discussion of original research papers on all aspects of grammar learning. ICGI, which has been organized bi-annually since early nineties, will be hosted this time at the NYC SUNY Global Center on Park Avenue, New-York, USA. ICGI 2020 is the place to present your work on learning formal grammars, finite state machines, context-free grammars, Markov models, or any models related to language theory, stochastic or not. Both theoretical work and experimental analyses are welcomed as submissions. This year we especially encourage submissions related to connectionist models such as neural networks, since the tutorials of the first day will focus on that topic. Invited Speakers - Dana Fisman (Ben-Gurion University) - Robert Frank (Yale University) - C. Lee Giles (Pennsylvania State University) - Guillaume Rabusseau (Université de Montréal) - Gail Weiss (Technion - Israel Institute of Technology) More details can be found on our designated webpage: https://icgi2020.lis-lab.fr/speakers/ Competition ICGI 2020 is hosting a shared task on morphological inflection. An example of English inflection is the conversion of the lemma run to its present participle, running. To participate in the shared task, you will build a system that can learn to solve inflection problems. More details at https://aryamccarthy.github.io/icgi2020/ Topics of interest - Theoretical aspects of grammatical inference: learning paradigms, learnability results, complexity of learning - Empirical and theoretical research on query learning, active learning, and other interactive learning paradigms - Empirical and theoretical research on methods using or including, but not limited to, spectral learning, state-merging, distributional learning, statistical relational learning, statistical inference and/or Bayesian 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. - Learning with contextualized data: for instance, Grammatical inference from strings or trees paired with semantics representations, or learning by situated agents and robots. - 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. - 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. Types of Contributions We welcome three types of papers: - Formal and/or technical papers describe original solutions (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 or approaches, open problems. Current limits can be discussed. In all cases rigor in presentation will be required. Such papers must describe precisely the situation, problem, or challenge addressed, and demonstrate how current methods, tools, 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. Selected authors will be encouraged to submit an extended version of their work to an upcoming special issue of an international journal (to be announced). Guidelines for authors Accepted papers will be published within the Proceedings of Machine Learning Research series (http://proceedings.mlr.press/). They must be submitted in pdf format through EasyChair. The total length of the paper should not exceed 12 pages on A4-size paper. 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 of final published version. Important Dates Deadline for submissions is: May 1, 2020 Notification of acceptance: June 15, 2020 Camera-ready copy: July 15, 2020 Conference: August 26-28, 2020 Conference Chairs: Jane Chandlee, Haverford College Rémi Eyraud, QARMA team, Aix-Marseille University Jeffrey Heinz, Stony Brook University Adam Jardine, Rutgers University Program committee consists of more than thirty internationally recognizable researchers (names can be found on our website: https://icgi2020.lis-lab.fr/committees/). For any enquiries regarding general issues, the program, or if you are a potential sponsor, please contact one of the conference chair. We look forward to seeing you at ICGI 2020. Sincerely, Adam, Jane, Jeffrey, Rémi -- [LOGIC] mailing list http://www.dvmlg.de/mailingliste.html Archive: http://www.illc.uva.nl/LogicList/ provided by a collaboration of the DVMLG, the Maths Departments in Bonn and Hamburg, and the ILLC at the Universiteit van Amsterdam