Colin de la Higuera, Author at unitwin-unoe

Colin de la Higuera

Colin de la Higuera is a professor and a researcher in computer science at Nantes University and within the Laboratory of Digital Sciences of Nantes (LS2N). He is the holder of the UNESCO Chair RELIA (Open Educational Resources and Artificial Intelligence).

His research work focuses on machine learning, a field of Artificial Intelligence in which he collaborates with researchers around the world. He is internationally recognized for his vision and inspiring mentorship in the field of open education, including linking these issues to those of artificial intelligence.

He led the creation of the Unitwin Network on Open Education, which brings together 16 institutions from 14 countries.
Nantes Université, France ;

Publications referenced on ORCID (83)

Journal articles (28)

[1] Teachers, learners, and oracles, in Notre Dame Journal of Formal Logic ; 2019. doi

[2] Computing the Expected Edit Distance from a String to a Probabilistic Finite-State Automaton, in International Journal of Foundations of Computer Science ; 2017. doi

[3] A canonical semi-deterministic transducer, in Fundamenta Informaticae ; 2016. doi

[4] Grammatical inference of PCFGs applied to language modelling and unsupervised parsing, in Fundamenta Informaticae ; 2016. doi

[5] Preface, in Fundamenta Informaticae ; 2016. doi

[6] On the complexity of submap isomorphism and maximum common submap problems, in Pattern Recognition ; 2015. doi

[7] Introduction to the special issue on grammatical inference, in Machine Learning ; 2014. doi

[8] PAutomaC: A probabilistic automata and hidden Markov models learning competition, in Machine Learning ; 2014. doi

[9] The most probable string: An algorithmic study, in Journal of Logic and Computation ; 2014. doi

[10] Polynomial algorithms for open plane graph and subgraph isomorphisms, in Theoretical Computer Science ; 2013. doi

[11] Introduction to the special topic on grammar induction, Representation of language and language learning, in Journal of Machine Learning Research ; 2011.

[12] Polynomial algorithms for subisomorphism of nD open combinatorial maps, in Computer Vision and Image Understanding ; 2011. doi

[13] ERS, an online evaluation system for assessing the reputation of scientific journals,ERS, une plate-forme bibliométrique présentation d’une plate-forme web de mesure bibliométrique des revues en informatique, in Revue d’Intelligence Artificielle ; 2009. doi

[14] Introduction: Special issue on applications of grammatical inference, in Applied Artificial Intelligence ; 2008. doi

[15] Learning balls of strings from edit corrections, in Journal of Machine Learning Research ; 2008.

[16] LARS: A learning algorithm for rewriting systems, in Machine Learning ; 2007. doi

[17] A bibliographical study of grammatical inference, in Pattern Recognition ; 2005. doi

[18] Probabilistic finite-state machines – Part I, in IEEE Transactions on Pattern Analysis and Machine Intelligence ; 2005. doi

[19] Probabilistic finite-state machines – Part II, in IEEE Transactions on Pattern Analysis and Machine Intelligence ; 2005. doi

[20] Inference of ω-languages from prefixes, in Theoretical Computer Science ; 2004. doi

[21] Machine learning: Introduction, in Machine Learning ; 2001. doi

[22] Apprentissage de programmes logiques par inférence grammaticale, in Revue d’Intelligence Artificielle ; 2000.

[23] Topology of strings: Median string is NP-complete, in Theoretical Computer Science ; 2000. doi

[24] Optimal linguistic decoding is a difficult computational problem, in Pattern Recognition Letters ; 1999. doi

[25] Characteristic Sets for Polynomial Grammatical Inference, in Machine Learning ; 1997. doi

[26] Drawing and encoding two-dimensional posets, in Theoretical Computer Science ; 1997. doi

[27] On sets of terms: A study of a generalisation relation and of its algorithmic properties, in Fundamenta Informaticae ; 1996. doi

[28] Computing the jump number on semi-orders is polynomial, in Discrete Applied Mathematics ; 1994. doi

Conference papers (18)

[29] What should I learn next? Ranking educational resources, in Proceedings – 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021 ; 2021. doi

[30] Promoting Open Education through Gamification in Higher Education: The OpenGame project, in ACM International Conference Proceeding Series ; 2020. doi

[31] SUM’20: State-based user modelling, in WSDM 2020 – Proceedings of the 13th International Conference on Web Search and Data Mining ; 2020. doi

[32] A simple linear-time algorithm for computing the centroid and canonical form of a plane graph and its applications, in Leibniz International Proceedings in Informatics, LIPIcs ; 2018. doi

[33] The paper or the video: Why choose?, in WWW 2015 Companion – Proceedings of the 24th International Conference on World Wide Web ; 2015. doi

[34] PCFG induction for unsupervised parsing and language modelling, in EMNLP 2014 – 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference ; 2014. doi

[35] Computing the most probable string with a probabilistic finite state machine, in FSMNLP 2013 – Proceedings of the 11th International Conference on Finite State Methods and Natural Language Processing ; 2013. doi

[36] Learning probabilistic subsequential transducers from positive data, in ICAART 2013 – Proceedings of the 5th International Conference on Agents and Artificial Intelligence ; 2013. doi

[37] Finding the most probable string and the consensus string: An algorithmic study, in IWPT 2011 – Proceedings of the 12th International Conference on Parsing Technologies ; 2011. doi

[38] Formal and empirical grammatical inference, in ACL HLT 2011 – 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of Student Session ; 2011. doi

[39] A contextual normalised edit distance, in Proceedings – First International Workshop on Similarity Search and Applications, SISAP 2008 ; 2008. doi

[40] A contextual normalised edit distance, in Proceedings – International Conference on Data Engineering ; 2008. doi

[41] Introducing domain and typing bias in automata inference, in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) ; 2004. doi

[42] Learning stochastic finite automata, in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) ; 2004. doi

[43] Representing languages by learnable rewriting systems, in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) ; 2004. doi

[44] Inferring deterministic linear languages, in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) ; 2002. doi

[45] Learning languages with help, in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) ; 2002. doi

[46] On sufficient conditions to identify in the limit classes of grammars from polynomial time and data, in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) ; 2002. doi

Books and books sections (34)

[47] Ethical Principles in Industry 4.0 Automated Decision-Making Systems, in Studies in Computational Intelligence ; 2022. doi

[48] Foreword by colin de la higuera, in Lecture Notes in Educational Technology ; 2020. doi

[49] Learning Unions of k-Testable Languages, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2019. doi

[50] Computing the expected edit distance from a string to a PFA, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2016. doi

[51] Computing the overlaps of two maps, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2016. doi

[52] Grammatical Inference for Computational Linguistics, in Synthesis Lectures on Human Language Technologies ; 2016. doi

[53] Learning grammars and automata with queries, in Topics in Grammatical Inference ; 2016. doi

[54] Grammatical inference: Learning automata and grammars, in Grammatical Inference: Learning Automata and Grammars ; 2013. doi

[55] On the complexity of submap isomorphism, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2013. doi

[56] ε-Removal by loop reduction for finite-state automata over complete semirings, in Language and Logos: Studies in Theoretical and Computational Linguistics ; 2012. doi

[57] Computational Language Learning (Update of Chapter 15), in Handbook of Logic and Language ; 2011. doi

[58] Extracting plane graphs from images, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2010. doi

[59] Grammatical inference algorithms in MATLAB, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2010. doi

[60] Learning finite state machines, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2010. doi

[61] Zulu: An interactive learning competition, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2010. doi

[62] A Polynomial Algorithm for Submap Isomorphism Application to Searching Patterns in Images, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2009. doi

[63] ERS: Evaluating reputations of scientific journals, in Frontiers in Artificial Intelligence and Applications ; 2008. doi

[64] Learning languages from bounded resources: The case of the DFA and the balls of strings, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2008. doi

[65] Learning balls of strings with correction queries, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2007. doi

[66] Identification in the limit of systematic-noisy languages, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2006. doi

[67] Learning stochastic finite automata for musical style recognition, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2006. doi

[68] Ten open problems in grammatical inference, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2006. doi

[69] Distances between distributions: Comparing language models, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2004. doi

[70] Improving probabilistic automata learning with additional knowledge, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2004. doi

[71] Identification with probability one of stochastic deterministic linear languages, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2003. doi

[72] Inference of ω-languages from prefixes, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2001. doi

[73] Computational complexity of problems on probabilistic grammars and transducers, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2000. doi

[74] Current trends in grammatical inference, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2000. doi

[75] Identification in the limit with probability one of stochastic deterministic finite automata, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 2000. doi

[76] Learning stochastic finite automata from experts, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 1998. doi

[77] Characteristic sets for polynomial grammatical inference, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 1996. doi

[78] Identification of dfa: Data-dependent versus data-independent algorithms, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 1996. doi

[79] Preface, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 1996. doi

[80] Learning from recursive, tree structured examples, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 1994. doi

Other publications (3)

[81] Marginal Inference queries in Hidden Markov Models under context-free grammar constraints, in arXiv ; 2022. doi

[82] Distance and equivalence between finite state machines and recurrent neural networks: Computational results, in arXiv ; 2020. doi

[83] Learning Unions of k-Testable Languages ^?, in arXiv ; 2018. doi