A few words about publication policy might be of interest here, since this is a topical issue in science right now.

IMHO, ordering of authors should not be taken into consideration: either you wrote the paper (and then you are an author: the paper would not be the same without you) or you did not (and then your name is not on it, maybe in the aknowledgments section if you participated undirectly); deciding who contributed the most is not relevant (what's the most important? The number of lines written? The initial idea? The Developpement of the proofs? Something else?). Mathematicians know how important it is to give author's names in alphabetical order. I have always tried to follow that path, with exceptions (due to evaluations done by non-researchers of my collaborators)

Conference or Journal? In "old" sciences, journals are the only thing that matter. In Computer Science, it is the contrary: it is often harder to be published in great conferences than in great journals. Once you know that, most of the bibliometrics used by universities today are not relevant...

Impact Factor and other bù||$h!t$. Bibliometry is bad for science. It has the consequence to slow down progress and to put power into the hand of big editors who are making a lot of money on the work of researchers (see this paper and the citations it contains for details, for instance). In Computer Science, we do not want to use IF because our best journals and conferences have low IF. And then we have things like the CORE ranking: the French governement uses it to evaluate French researchers in CS while it is made by some Australian private associations. As some fields of CS were not represented in Australia this killed these fields in France. This literally implies that the French research policy about CS is decided in Australia...

The only thing that should really matter is open access. I mean real open access, where neither the authors nor the readers have to pay. We are doing the research, we are writing the papers, we are reviewing them, we are even editing them: why should we pay to access them? There exist a lot of places (conference proceedings and journals) where you can publish CS work that are open-access.

"Efficiency in the identification in the limit paradigm", Rémi Eyraud, Jeffrey Heinz, Ryo Yoshinaka, in Topics in Grammatical Inference, Jeffrey Heinz and José M. Sempere (Eds.), Springer, 2016, in press (personal version).

- Fundamenta Informaticae, "Designing and Learning Substitutable Plane Graph Grammars", Rémi Eyraud, Jean-Christophe Janodet, Tim Oates, Frédéric Papadopoulos,
*November 2016*, (submitted version). - Transactions of the Association for Computational Linguistics, "Learning Strictly Local Subsequential Functions", Jane Chandlee, Rémi Eyraud, Jeffrey Heinz
*November 2014*, (online version, bibtex). - Machine Learning Journal, "PAutomaC: a probabilistic automata and hidden Markov models learning competition", Sicco Verwer, Rémi Eyraud, Colin de la Higuera.
*July 2014*, (.pdf,.bibtex). - Journal of Machine Learning Research, "Using Contextual Representations to Efficiently Learn Context-Free Languages", Alexander Clark, Rémi Eyraud, Amaury Habrard.
*October 2010*, (online version). - Journal of Machine Learning Research, "Polynomial identification in the limit of context-free substitutable languages", Alexander Clark, Rémi Eyraud.
*August 2007*(on line version). - Machine Learning Jounal, "LARS: a Learning Algorithm for Rewriting Systems", Rémi Eyraud, Colin de la Higuera, Jean-Christophe Janodet.
*January 2007*(.pdf, bibtex)

- ICGI'16: "Sp2Learn: A Toolbox for the Spectral Learning of Weighted Automata", Denis Arrivault, Dominique, Benielli, François Denis, Rémi Eyraud, , 13th International Conference on Grammatical Inference (.pdf)
- ICGI'16: "Results of the Sequence PredIction ChallengE (SPiCe): a Competition on Learning the Next Symbol in a Sequence", Borja Balle, Rémi Eyraud, Franco M. Luque, Ariadna Quattoni, Sicco Verwer, 13th International Conference on Grammatical Inference (.pdf)
- MoL'15: "Output Strictly Local Functions", Jane Chandlee, Rémi Eyraud, Jeffrey Heinz , 14th Meeting on the Mathematics of Language (.pdf, bibtex).
- ICGI'14: "Very efficient learning of structured classes of subsequential functions from positive data", Adam Jardine, Jane Chandlee, Rémi Eyraud, Jeffrey Heinz , The 12th International Colloquium on Grammatical Inference (.pdf, bibtex).
- MoL'15: "Output Strictly Local Functions", Jane Chandlee, Rémi Eyraud, Jeffrey Heinz , 14th Meeting on the Mathematics of Language (.pdf, bibtex).
- ICGI'14: "Very efficient learning of structured classes of subsequential functions from positive data", Adam Jardine, Jane Chandlee, Rémi Eyraud, Jeffrey Heinz , The 12th International Colloquium on Grammatical Inference (.pdf, bibtex).
- ICGI'12: "Learning Substitutable Binary Plane Graph Grammars", Rémi Eyraud, Jean-Christophe Janodet, Tim Oates , The 11th International Colloquium on Grammatical Inference (.pdf).
- ICGI'12: "Results of the PAutomaC Probabilistic Automaton Learning Competition ", Sicco Verwer, Rémi Eyraud, Colin de la Higuera , The 11th International Colloquium on Grammatical Inference (.pdf).
- ICGI'08: "A Polynomial Algorithm for the Inference of Context Free Languages", Alexander Clark, Rémi Eyraud, Amaury Habrard , The 9th International Colloquium on Grammatical Inference (.pdf).
- CoNLL'06: "Learning Auxiliary Fronting with Grammatical Inference", Alexander Clark, Rémi Eyraud The 10th Conference on Computational Natural Language Learning (Bibtex, .pdf).
- CogSci'06: "Learning Auxiliary Fronting with Grammatical Inference", Alexander Clark, Rémi Eyraud The 28th Annual Conference of the Cognitive Science Society, poster (Bibtex, .pdf).
- ALT'05: "Identification in the Limit of Substitutable Context-Free Languages", Alexander Clark, Rémi Eyraud The 16th International Conference on Algorithmic Learning Theory (Bibtex, .pdf, talk).
- ICGI'04: "Representing Languages by Learnable Rewriting Systems", Rémi Eyraud, Colin de la Higuera, Jean-Christophe Janodet, 7th International Colloquium on Grammatical Inference (Bibtex, .ps, talk). An on line version of the arlgorithm described in this article is available here.

- Invited Talk (2014 ELC Workshop on Learning Theory and Complexity), Efficiency bounds in the identification in the limit paradigm, Rémi Eyraud, (slides).
- Paper (EACL 2009 workshop on Computational Linguistic Aspects of Grammatical Inference, Athens, Greece, 2009), A note on contextual binary feature grammars, Alexander Clark, Rémi Eyraud, Amaury Habrard.
- Communication TML (workshop Teaching Machine Learning, Saint-Etienne, France, 2008), "Spring School in Machine Learning. Teaching experiences", with Cécile Capponi, François Denis, Amaury Habrard et Liva Ralaivola (pdf).
- Poster ML4NLP (workshop Machine Learning for Natural Languages Processing, Amsterdam, 2007) How to learn context-free grammars
- Poster Benelearn 2007 (Benelux machine learning conference, Amsterdam, 2007) Two methods to learn context-free languages
- Poster CAp'06 (Conférence en Apprentissage, TRégastel, 2006) Inférence grammaticale de langages hors-contextes
- Msc. report, "Inférence d'Automates par Fission d'Etats", 26th june 2003, Ecole des mines de Saint-Etienne (ps) (french only)

- Thesis, Inférence Grammaticale de Langages Hors-Contextes, Rémi Eyraud
*defended the 22/11/2006 at Saint-Etienne*(.pdf (french))