Students

PhD students

Niels Cobat (2024 - 2027)
- Title: Analyse et optimisation des fichiers d’impression 3D à l’aide de méthodes d’apprentissage automatique
- Funding: Univ-Rennes
- Co-advisors: Damien Hardy (PACAP), Romaric Gaudel
Dimitri Lereverend (2023 - 2026)
- Title: Privacy Preserving Decentralized Through Model Fragmentation
- Funding: Inria
- Co-advisors: Davide Frey (WIDE), Romaric Gaudel
- Supervisors: Davide Frey (WIDE), Romaric Gaudel, François Taïani (WIDE)
Paul Sevellec (2023 - 2026)
- Title: Explications de séries temporelles multivariées par contrefactuels
- Funding: Stellantis (Cifre)
- Co-advisors: Élisa Fromont (50%) and Romaric Gaudel (50%)
- Supervisors: Matteo Sammarco (Stellantis), Élisa Fromont, Romaric Gaudel, Laurence Rozé
Camille-Sovanneary Gauthier (2019 - 2022) — homepage, LinkedIn
- Title: Learning to Rank in a bandit setting
- Funding: Louis Vuitton (Cifre)
- Co-advisors: Élisa Fromont (50%) and Romaric Gaudel (50%)
- Advisors at Louis Vuitton: Bruno Guilbot and Éliot Barril
Anh Duong NGUYEN (2018 - 2019)
- Title: Compression Based Pattern Mining
- ‘Direction’: Alexandre Termier
- Co-advisors: Romaric Gaudel (50%), Peggy Cellier (25%), and Alexandre Termier (25%)
- Ended after 1 year
Frédéric Guillou (2013 - 2016) — LinkedIn
- Title: Sequential Recommender Systems
- ‘Direction’: Philippe Preux
- Co-advisors: Jérémie Mary (50%), Romaric Gaudel (50%)

Undergraduate/graduate internships

2022: Niels Cobat (L3 internship, 2 months) Estimation précise du temps d’impression 3D par machine learning
2022: Raphaël Giraud (M1 internship, 2.5 months) Local search for combinatorial bandit algorithms
2022 – 2023: Gauvain Thomas (M1, half a day a week for the whole year) Consensus de brins d’ADN par diagonalisation de matrices de Hankel
2022 – 2023: Aymeric Behaegel (M1, half a day a week for the whole year) Bandits-manchots combinatoires unimodaux
2022: Arthur Katossky (M1 internship, 2 months) Local explanation of learned models
2022: François Olivier (L3 internship, 2 months) Bandits manchots combinatoires uni-modaux
2022: Paul Mboucheko Mboucheko (M1 internship, 2 months) Consensus de brins d’ADN par diagonalisation de matrices de Hankel
2021 – 2022: Matthieu Rodet (M1, half a day a week for the whole year) Combinatorial Unimodal Bandits
2020: Maxime Heuillet (M2 internship, 1 month) A neural network approach to privacy preserving multi-engagement prediction on Twitter
2020: Aser Boammani Lompo (M1 internship, 2 months) Unimodal bandit for list-wise recommendation
2020: Alex Georget (M1 internship, 3 months) Unimodal bandit for list-wise recommendation
2020: Théo Velletaz (M1 internship, 3 months) Interpretation of continuous models (co-advised with L. Galárraga)
2019: Vaishnavi Bhargava (Bachelor internship, 4 months) Automatic Neighborhood Design for Localized Model-interpretation (co-advised with L. Galárraga)
2018: Grégoire Pacreau (L3 internship, 1.5 months) non-crude MDL based Pattern Mining (co-advised with A. Termier and P. Cellier)
2018: Erwan Bourran (M2 internship) MCTS approach for MDL based Pattern Mining (co-advised with A. Termier and P. Cellier)
2017 – 2018: Hippolyte Bourel, Nathan Koskas and Jimmy Petit (M1, 1 day a week for the whole year) Review of Time Series prediction models (co-advised with É. Fromont and L. Rozé)
2017: Leonardo Cella (post-M2, 1 week a month for 4 months) Bandit based Recommender Systems
2016: Rida Darmal and Zakaria Hadjadji (M1, 1 day a week for a semester) Recommender Systems with meta-data
2016: Pierrick Deshayes and Amine El-Mabkhout (M1, 1 day a week for a semester) Rank adaptation for Sequential Recommender Systems
2016: Mehdi Abbana Bennani (M1 internship, 3 months) Neural Network based Recommender Systems
2014 – 2015: Erivan Cogez (L3, half a day a week for a semester) Sequential Recommender Systems
2014: Mathias Sablé Meyer (L3 internship, 1.5 months) Sequential Recommender Systems
2012: Florian Gas (M2, half a day a week for a semester) Extreme Values Theory for Extremely Multi-Armed Bandits