Mining Implications From Data. - Ahcene Boubekki; Daniel Bengs (LWA, 2014)

Constrained independence for detecting interesting patterns. - Thomas Delacroix; Ahcene Boubekki; Philippe Lenca; Stéphane Lallich (DSAA, 2015)

Generalising IRT to Discriminate Between Examinees. - Ahcène Boubekki; Ulf Brefeld; Thomas Delacroix (EDM, 2015)

Toward Data-Driven Analyses of Electronic Text Books. - Ahcène Boubekki; Ulf Kröhne; Frank Goldhammer; Waltraud Schreiber; Ulf Brefeld (EDM, 2015)

Data-Driven Analyses of Electronic Text Books. - Ahcène Boubekki; Ulf Kröhne; Frank Goldhammer; Waltraud Schreiber; Ulf Brefeld (Solving Large Scale Learning Tasks, 2016)

Frame-based Data Factorizations. - Sebastian Mair 001; Ahcène Boubekki; Ulf Brefeld (ICML, 2017)

Propagating Maximum Capacities for Recommendation. - Ahcène Boubekki; Ulf Brefeld; Cláudio Leonardo Lucchesi; Wolfgang Stille (KI, 2017)

Infinite Mixtures of Markov Chains. - Jan Reubold; Ahcène Boubekki; Thorsten Strufe; Ulf Brefeld (NFMCP@PKDD/ECML, 2017)

Mining User Trajectories in Electronic Text Books. - Ahcène Boubekki; Shailee Jain; Ulf Brefeld (EDM, 2018)

Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring. - Irina Pandarova; Torben Schmidt; Johannes Hartig; Ahcène Boubekki; Roger Dale Jones; Ulf Brefeld (Int. J. Artif. Intell. Educ., 2019)

Analysis of user behavior. - Ahcène Boubekki (2020)

Joint optimization of an autoencoder for clustering and embedding. - Ahcène Boubekki; Michael Kampffmeyer; Ulf Brefeld; Robert Jenssen (Mach. Learn., 2021)

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. - Srishti Gautam; Ahcène Boubekki; Stine Hansen; Suaiba Amina Salahuddin; Robert Jenssen; Marina M.-C. Höhne; Michael Kampffmeyer (NeurIPS, 2022)

Clinically Relevant Features for Predicting the Severity of Surgical Site Infections. - Ahcène Boubekki; Jonas Nordhaug Myhre; Luigi Tommaso Luppino; Karl Øyvind Mikalsen; Arthur Revhaug; Robert Jenssen (IEEE J. Biomed. Health Informatics, 2022)

Supercm: Revisiting Clustering for Semi-Supervised Learning. - Durgesh Singh 003; Ahcène Boubekki; Robert Jenssen; Michael C. Kampffmeyer (ICASSP, 2023)

RELAX: Representation Learning Explainability. - Kristoffer K. Wickstrøm; Daniel J. Trosten; Sigurd Løkse; Ahcène Boubekki; Karl Øyvind Mikalsen; Michael C. Kampffmeyer; Robert Jenssen (Int. J. Comput. Vis., 2023)

Selective Imputation for Multivariate Time Series Datasets With Missing Values. - Ane Blázquez-García; Kristoffer Wickstrøm; Shujian Yu; Karl Øyvind Mikalsen; Ahcène Boubekki; Angel Conde; Usue Mori; Robert Jenssen; José Antonio Lozano 001 (IEEE Trans. Knowl. Data Eng., 2023)

Pantypes: Diverse Representatives for Self-Explainable Models. - Rune D. Kjærsgaard; Ahcène Boubekki; Line H. Clemmensen (AAAI, 2024)

EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods. - Benedict Clark; Rick Wilming; Artur Dox; Paul Eschenbach; Sami Hached; Daniel Jin Wodke; Michias Taye Zewdie; Uladzislau Bruila; Marta Oliveira; Hjalmar Schulz; Luca Matteo Cornils; Danny Panknin; et al. (CoRR, 2024)

Leveraging Activations for Superpixel Explanations. - Ahcène Boubekki; Samuel G. Fadel; Sebastian Mair 001 (CoRR, 2024)

Explainable AI needs formal notions of explanation correctness. - Stefan Haufe; Rick Wilming; Benedict Clark; Rustam Zhumagambetov; Danny Panknin; Ahcène Boubekki (CoRR, 2024)

Explaining the Impact of Training on Vision Models via Activation Clustering. - Ahcène Boubekki; Samuel G. Fadel; Sebastian Mair 001 (CoRR, 2024)

Prototypical Self-Explainable Models Without Re-training. - Srishti Gautam; Ahcene Boubekki; Marina M.-C. Höhne; Michael Kampffmeyer (Trans. Mach. Learn. Res., 2024)

EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode. - Durgesh Singh 003; Ahcene Boubekki; Qing Cao; Svein Arne Aase; Robert Jenssen; Michael Kampffmeyer (CoRR, 2025)

SuperCM: Improving Semi-Supervised Learning and Domain Adaptation through differentiable clustering. - Durgesh Singh 003; Ahcène Boubekki; Robert Jenssen; Michael Kampffmeyer (CoRR, 2025)

SuperCM: Improving semi-supervised learning and domain adaptation through differentiable clustering. - Durgesh Kumar Singh; Ahcène Boubekki; Robert Jenssen; Michael Kampffmeyer (Pattern Recognit., 2026)