Publications

See also my publications on HAL and Google Scholar.

International Journal Papers

• Stéphan Clémençon, Charanpal Dhanjal, and Romaric Gaudel (2014). Efficient Eigen-updating for Spectral Graph Clustering (arXiv). Neurocomputing, 131:440–452.

International Conference Papers

• Paul Sevellec, Elisa Fromont, Romaric Gaudel, Laurence Rozé, Matteo Sammarco (2025). Plausible Conditional Generation-based Counterfactual Explanations for Multivariate Times Series Classification (pdf on HAL). In Proc. of European Conference on Artificial Intelligence (ECAI’25).
• Sayan Biswas, Davide Frey, Romaric Gaudel, Anne-Marie Kermarrec, Dimitri Lerévérend, Rafael Pires, Rishi Sharma, François Taïani (2025). Low-Cost Privacy-Preserving Decentralized Learning (pdf on arXiv). In Proc. of Privacy Enhancing Technologies (PETS/PoPETs’25).
• Gwladys Kelodjou, Laurence Rozé, Véronique Masson, Luis Galárraga, Romaric Gaudel, Maurice Tchuenté, Alexandre Termier (2024). Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection (pdf on arXiv). AAAI’24.
• Camille-Sovanneary Gauthier, Romaric Gaudel, Élisa Fromont (2022). UniRank: Unimodal Bandit Algorithms for Online Ranking (pdf). In Proc. of International Conference on Machine Learning (ICML’22).
• Romaric Gaudel, Luis Galárraga, Julien Delaunay, Laurence Rozé, and Vaishnavi Bhargava (2022). S-LIME: Reconciling Locality and Fidelity in Linear Explanations. In Proc. of the Symposium on Intelligent Data Analysis (IDA’22).
• Camille-Sovanneary Gauthier, Romaric Gaudel, Élisa Fromont, and Boammani Lompo (2021). Parametric Graph for Unimodal Ranking Bandit (pdf on HAL). In Proc. of International Conference on Machine Learning (ICML’21).
• Camille-Sovanneary Gauthier, Romaric Gaudel, and Élisa Fromont (2021). Bandit Algorithm for Both Unknown Best Position and Best Item Display on Web Pages (pdf on HAL). In Proc. of the Symposium on Intelligent Data Analysis (IDA’21).
• Frédéric Guillou, Romaric Gaudel, and Philippe Preux (2016). Sequential Collaborative Ranking Using (No-)Click Implicit Feedback (pdf on HAL). International Conference on Neural Information Processing (ICONIP’16).
• Frédéric Guillou, Romaric Gaudel, and Philippe Preux (2016). Scalable Explore-Exploit Collaborative Filtering. Pacific Asia Conference on Information Systems (PACIS’16).
• Charanpal Dhanjal, Romaric Gaudel, and Stéphan Clémençon (2015). Collaborative Filtering with Localised Ranking (publisher pdf). AAAI’15, pp. 2554–2560.
• Charanpal Dhanjal, Romaric Gaudel, and Stéphan Clémençon (2014). Online Matrix Completion Through Nuclear Norm Regularisation (arXiv). SIAM International Conference on Data Mining (SDM’14), pp. 623–631.
• Stéphan Clémençon, Romaric Gaudel, and Jérémie Jakubowicz (2011). Clustering Rankings in the Fourier Domain (publisher pdf). In Proc. of European Conference on Machine Learning (ECML’11), pp. 343–358.
• Romaric Gaudel, Jean-Baptiste Hoock, Julien Pérez, Nataliya Sokolovska, and Olivier Teytaud (2010). A Principled Method for Exploiting Opening Books (pdf on HAL). In Proc. of International Conference on Computers and Games (CG’10).
• Romaric Gaudel, and Michèle Sebag (2010). Feature Selection as a one-player game (pdf). In Proc. of International Conference on Machine Learning (ICML’10).
• Romaric Gaudel, Michèle Sebag, and Antoine Cornuéjols (2007). A Phase Transition-based perspective on Multiple Instance kernels. In Proc. of International Conference on Inductive Logic Programming (ILP’07) (short paper, pp. 112–121).

Workshops, Poster Track

• Leonardo Cella, Romaric Gaudel, Paolo Cremonesi (2017). Kernalized Collaborative Contextual Bandits. Poster Track of the 11th ACM Conference on Recommender Systems (RecSys’17).
• Florian Strub, Romaric Gaudel and Jérémie Mary (2016). Hybrid Recommender System based on Autoencoders. RecSys Workshop on Deep Learning for Recommender Systems (DLRS’16).
• Frédéric Guillou, Romaric Gaudel, and Philippe Preux (2016). Large-scale Bandit Recommender System. International Workshop on Machine Learning, Optimization and Big Data (MOD’16).
• Frédéric Guillou, Romaric Gaudel, and Philippe Preux (2015). Collaborative Filtering as a Multi-Armed Bandit. NIPS Workshop: Machine Learning for eCommerce.
• Jérémie Mary, Romaric Gaudel, and Philippe Preux (2015). Bandits and Recommender Systems. International Workshop on Machine Learning, Optimization and Big Data (MOD’15).
• Frédéric Guillou, Romaric Gaudel, Jérémie Mary, and Philippe Preux (2014). User Engagement as Evaluation: a Ranking or a Regression Problem? Workshop of the 2014 Recommender Systems Challenge.
• Charanpal Dhanjal, Romaric Gaudel, and Stéphan Clémençon (2011). Incremental Spectral Clustering with the Normalised Laplacian (pdf). 3rd NIPS Workshop on Discrete Optimization in Machine Learning (DISCML’11).
• Romaric Gaudel, and Michèle Sebag (2009). Feature Selection as a one-player game. 2nd NIPS Workshop on Optimization for Machine Learning (OPT’09).

National Conference Papers (with review process)

• Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont, and Boammani Aser Lompo (2021). Ordonnancement d’objets par bandits unimodaux sur des graphes paramétriques. In Proc. of Conférence francophone sur l’apprentissage automatique (CAp’21).
• Camille-Sovanneary Gauthier, Romaric Gaudel, and Elisa Fromont (2020). Bandits manchots avec échantillonnage de Thompson pour des recommandations multiples suivant un modèle fondé sur les positions (preprint pdf). In Proc. of Conférence francophone sur l’apprentissage automatique (CAp’20).
• Romaric Gaudel, and Stéphan Clémençon (2011). Le Filtrage Collaboratif vu comme un problème de Consensus d’Ordonnancements. In Proc. of Conférence Francophone d’Apprentissage (CAp’11), pp. 167–182.
• Romaric Gaudel, and Michèle Sebag (2011). Feature Selection as a one-player game. In Proc. of Conférence Francophone d’Apprentissage (CAp’11), pp. 183–198.
• Romaric Gaudel, Michèle Sebag, and Antoine Cornuéjols (2007). A Phase Transition-based perspective on Multiple Instance kernels. In Proc. of Conférence Francophone d’Apprentissage (CAp’07), 14 pages, Presses Universitaires de Grenoble, acceptance rate 40 %.

PhD Thesis

• Romaric Gaudel (2010). Paramètres d’ordre et sélection de modèles en apprentissage : caractérisation des modèles et sélection d’attributs (pdf). PhD dissertation, Univ. Paris-Sud.

Shared files: Google Drive folder.