Jill-Jênn Vie
Researcher at Inria
Preprints
Vie, Jill-Jênn, and Hisashi Kashima. n.d.a. “Encode & Decode:
Generalizing Deep Knowledge Tracing and Multidimensional Item Response
Theory.” http://jiji.cat/bigdata/edm2019_submission.pdf.
———. n.d.b. “Fast Variational Learning of Factorization Machines
for Large-Scale Recommender Systems.” http://jiji.cat/bigdata/vie2019vfm.pdf.
Journal Articles
In 2020, I worked with Paris Hospitals (AP-HP) as part of the Inria-Covid Task Force.
Bergner, Yoav, Peter Halpin, and Jill-Jênn Vie. 2022.
“Multidimensional Item Response Theory in the Style of
Collaborative Filtering.” Psychometrika 87 (1): 266–88.
https://rdcu.be/cAcgu.
Brat, Gabriel A, Griffin M Weber, Nils Gehlenborg, Paul Avillach, Nathan
P Palmer, Luca Chiovato, James Cimino, et al. 2020. “International
Electronic Health Record-Derived COVID-19 Clinical Course Profiles: The
4ce Consortium.” Npj Digital Medecine 3 (1): 109. https://doi.org/10.1038/s41746-020-00308-0.
Khonsari, Roman Hossein, Mélodie Bernaux, Jill-Jênn Vie, Abdourahmane
Diallo, Nicolas Paris, Liem Binh Luong, Jamal Assouad, et al. 2021.
“Risks of early mortality and pulmonary
complications following surgery in patients with
COVID-19.” British Journal of Surgery, February.
https://doi.org/10.1093/bjs/znab007.
Vie, Jill-Jênn, Fabrice Popineau, Éric Bruillard, and Yolaine Bourda.
2018a. “Automated Test Assembly for Handling Learner Cold-Start in
Large-Scale Assessments.” International Journal of Artificial
Intelligence in Education, 1–16. https://rdcu.be/G30H.
———. 2018b. “Utilisation de tests adaptatifs dans les MOOC dans un
cadre de crowdsourcing.” Revue STICEF, Volume 24,
numéro 2, 2017. https://doi.org/10.23709/sticef.24.2.6.
Books
With Christoph Dürr,
we explain 128 algorithms for
preparing coding interviews & programming contests (Dürr and Vie 2016).
All 128 algorithms in
Python from the book are available on GitHub, in the Python package
tryalgo.
- Available in English: Competitive Programming with Python 🇬🇧 (Dürr and Vie 2020)
- In French: Programmation efficace 🇫🇷
- In Chinese: 高效算法 🇨🇳 (Dürr and Vie 2018)
- In Taiwanese: 培養與鍛鍊程式設計的邏輯腦 🇹🇼 (Dürr and Vie 2019)
We wrote another book to prepare French competitive exams: ENS and agrégation (Belghiti, Mansuy, and Vie 2016).
Belghiti, Ismael, Roger Mansuy, and Jill-Jênn Vie. 2016. Les clés pour
l’info : ENS Et Agrégation
(option D). Calvage et
Mounet.
Dürr, Christoph, and Jill-Jênn Vie. 2016. Programmation efficace:
Les 128 algorithmes qu’il faut avoir compris et codés dans
sa vie. Ellipses. https://tryalgo.org.
———. 2018. 高效算法: 竞赛、应试与提高必修128例. 人民邮电出版社.
https://book.douban.com/subject/30210075/.
———. 2019. 培養與鍛鍊程式設計的邏輯腦:
程式設計大賽的128個進階技巧(使用Python). 博碩文化股份. http://www.drmaster.com.tw/Bookinfo.asp?BookID=MP11906.
———. 2020. Competitive Programming in Python: 128 Algorithms to
Develop Your Coding Skills. Cambridge University Press.
Popineau, Fabrice, Michal Valko, and Jill-Jênn Vie, eds. 2018.
Proceedings of the 1st International Workshop Eliciting Adaptive
Sequences for Learning (WeASeL). CEUR Workshop Proceedings 1. https://humanlearn.io/proceedings/vol-1/.
Book Chapters
We wrote a chapter about recent advances in adaptive assessment (Vie et al. 2017) in a learning analytics book, where we identify similarities between cognitive diagnostic models and item response theory.
Vie, Jill-Jênn, Fabrice Popineau, Yolaine Bourda, and Éric Bruillard.
2017. “A Review of Recent Advances in Adaptive Assessment.”
In Learning Analytics: Fundaments, Applications, and Trends,
113–42. Springer. https://hal.archives-ouvertes.fr/hal-01488284/document.
Conference Proceedings
We unified educational data mining and psychometrics using factorization machines:
- Knowledge tracing machines (Vie and Kashima 2019) [tutorial] at AAAI 2019
Abdalla, Michel, and Jill-Jênn Vie. 2012. “Leakage-Resilient
Spatial Encryption.” In International Conference on
Cryptology and Information Security in Latin America, 78–99.
Springer.
Choffin, Benoı̂t, Fabrice Popineau, Yolaine Bourda, and Jill-Jênn Vie.
2019. “DAS3H: Modeling Student Learning and
Forgetting for Optimally Scheduling Distributed Practice of
Skills.” In Proceedings of the
Twelfth International Conference on Educational Data Mining (EDM
2019), 29–38. https://arxiv.org/abs/1905.06873.
Minn, Sein, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima, and Feida Zhu.
2022. “Interpretable Knowledge Tracing: Simple and Efficient
Student Modeling with Causal Relations.” In Proceedings of
AAAI 2022, in press. https://arxiv.org/abs/2112.11209.
Minn, Sein, Yi Yu, Michel Desmarais, Feida Zhu, and Jill-Jênn Vie. 2018.
“Deep Knowledge Tracing and Dynamic Student Classification for
Knowledge Tracing.” In Proceedings of the 18th
IEEE International Conference on Data Mining, 1182–87.
https://arxiv.org/abs/1809.08713.
Vie, Jill-Jênn, and Hisashi Kashima. 2019. “Knowledge Tracing Machines: Factorization Machines for
Knowledge Tracing.” In Proceedings of the 33th
AAAI Conference on Artificial Intelligence, 750–57. https://arxiv.org/abs/1811.03388.
Vie, Jill-Jênn, Fabrice Popineau, Yolaine Bourda, and Éric Bruillard.
2016. “Adaptive Testing Using a General Diagnostic Model.”
In European Conference on Technology Enhanced Learning, 331–39.
Springer.
Vie*, Jill-Jênn, Tomas Rigaux*, and Sein Minn. 2022.
“Privacy-Preserving Synthetic Educational Data Generation.”
In Proceedings of EC-TEL 2022, in press. https://hal.archives-ouvertes.fr/hal-03715416.
Other Publications
Vie, Jill-Jênn. 2018. “Deep Factorization
Machines for Knowledge Tracing.” In Proceedings of the Thirteenth Workshop on Innovative Use
of NLP for Building Educational Applications, 370–73. https://arxiv.org/abs/1805.00356.
Vie, Jill-Jênn, Fabrice Popineau, Jean-Bastien Grill, Éric Bruillard,
and Yolaine Bourda. 2015a. “Predicting Performance over
Dichotomous Questions: Comparing Models for Large-Scale Adaptive
Testing.” In 8th International Conference on Educational Data
Mining.
———. 2015b. “Prédiction de performance sur des
questions dichotomiques : comparaison de modèles pour des
tests adaptatifs à grande échelle.” In
Atelier Évaluation des Apprentissages et Environnements
Informatiques.
Vie, Jill-Jênn, Fabrice Popineau, Françoise Tort, Benjamin Marteau, and
Nathalie Denos. 2017. “A Heuristic Method for Large-Scale
Cognitive-Diagnostic Computerized Adaptive Testing.” In
Proceedings of the Fourth (2017) ACM Conference on Learning @
Scale, 323–26. ACM. https://github.com/jilljenn/las2017-wip/.
Vie, Jill-Jênn, Florian Yger, Ryan Lahfa, Basile Clement, Kévin Cocchi,
Thomas Chalumeau, and Hisashi Kashima. 2017. “Using Posters to Recommend Anime and Mangas in a
Cold-Start Scenario.” In 2017
14th IAPR International Conference on Document Analysis and Recognition
(ICDAR) – Second International Workshop on Comics Analysis, Processing
and Understanding, 03:21–26. https://arxiv.org/abs/1709.01584.
Popularization of Science
We wrote articles in the French magazines Quadrature (Vie 2009), Tangente (Vie 2014a) and GNU Linux Magazine (Vie 2018).
Vie, Jill-Jênn. 2009. “Un Algorithme de Composition
Musicale.” Quadrature, no. 72: 10–14. https://jill-jenn.net/_static/works/un-algorithme-de-composition-musicale.pdf.
———. 2014a. “Grolopin Et Les Plans Projectifs Finis.”
Tangente Hors-Série, no. 52: 128–31.
———. 2014b. “Langages Rationnels Et Automates Finis.”
Bibliothèque Tangente, no. 52: 52–55.
———. 2018. “Comment Coder Un Système de
Recommandation En Python : L’exemple de Mangaki.” GNU/Linux
Magazine Hors-Série, no. 94. https://mangaki.fr/static/mangaki-linuxmag.pdf.
Vie, Jill-Jênn, Alexandre Talon, and Arthur Charguéraud. 2014.
“Les Concours Informatiques Destinés Aux
Jeunes.” Tangente Hors-Série, no. 52: 22.