Jill-Jênn Vie

Researcher at Inria

Preprints

Vie, Jill-Jênn, and Hisashi Kashima. n.d. “Encode & Decode: Generalizing Deep Knowledge Tracing and Multidimensional Item Response Theory.” http://jiji.cat/bigdata/edm2019_submission.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://www.nature.com/articles/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.
Petria, Iulia, Samuel Albuquerque, Gael Varoquaux, Jill-Jênn Vie, Gilberto Velho, Gianluca Perseghin, Ronan Roussel, and Louis Potier. 2022. “424-P: Body-Weight Variability and Risk of Cardiovascular Outcomes in Type 1 Diabetes: Results from the DCCT/EDIC Studies.” Diabetes 71 (Supplement_1). https://hal.archives-ouvertes.fr/hal-03818459.
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.
Yordanov, Youri, Aurélien Dinh, Alexandre Bleibtreu, Arthur Mensch, François-Xavier Lescure, Erwan Debuc, Patrick Jourdain, Luc Jaulmes, Agnes Dechartres, and AP-HP/Universities/Inserm COVID-19 research collaboration. 2021. “Clinical Characteristics and Factors Associated with Hospital Admission or Death in 43103 Adult Outpatients with Coronavirus Disease 2019 Managed with the Covidom Telesurveillance Solution: A Prospective Cohort Study.” Clinical Microbiology and Infection 27 (8): 1158–66. https://www.sciencedirect.com/science/article/pii/S1198743X21001932.

Books

TryAlgo TryAlgo

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.

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.
Hsiao, I-Han Sharon, Shaghayegh Sherry Sahebi, François Bouchet, and Jill-Jênn Vie, eds. 2021. Proceedings of the 14th International Conference on Educational Data Mining. https://hal.science/hal-03918191.
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:

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 the AAAI Conference on Artificial Intelligence, 36:12810–18. 11. 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.
Vassoyan, Jean, Jill-Jênn Vie, and Pirmin Lemberger. 2023. “Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning.” In Proceedings of the Sixteenth International Conference on Educational Data Mining (EDM 2023), in press. https://arxiv.org/abs/2305.06398.
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 Hisashi Kashima. 2022. “Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems.” In 2022 IEEE International Conference on Big Data (Big Data), 5607–14. Los Alamitos, CA, USA: IEEE Computer Society. https://jiji.cat/bigdata/vie2022vfm.pdf.
Vie, J., T. Rigaux, and H. Kashima. 2022. “Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems.” In, 5607–14. Los Alamitos, CA, USA: IEEE Computer Society. https://doi.ieeecomputersociety.org/10.1109/BigData55660.2022.10020448.
Vie*, Jill-Jênn, Tomas Rigaux*, and Sein Minn. 2022. “Privacy-Preserving Synthetic Educational Data Generation.” In Proceedings of EC-TEL 2022. Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption, 393–406. https://hal.archives-ouvertes.fr/hal-03715416.

Other Publications

Choffin, Benoît, Fabrice Popineau, Yolaine Bourda, and Jill-Jênn Vie. 2021. “Evaluating DAS3H on the EdNet Dataset.” In AAAI 2021 - The 35th Conference on Artificial Intelligence / Imagining Post-COVID Education with AI. Virtual, United States. https://hal.archives-ouvertes.fr/hal-03175874.
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.