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DPPy: Sampling Determinantal Point Processes with Python

 Guillaume Gautier , Rémi Bardenet , Michal Valko
Journal of Machine Learning Research, 2019
Article dans une revue hal-01879424v1

Detection of different classes of carbapenemases: Adaptation and assessment of a phenotypic method applied to Enterobacteriaceae, Pseudomonas aeruginosa and Acinetobacter baumannii, and proposal of a new algorithm

 Guillaume Gautier , Thomas Guillard , Bianca Podac , Béatrice Berçot , Véronique Vernet-Garnier , et al.
Journal of Microbiological Methods, 2018, 147, pp.26-35. ⟨10.1016/j.mimet.2018.02.013⟩
Article dans une revue hal-02453683v1
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On sampling determinantal point processes

 Guillaume, Michel, Jean Gautier
Other. Centrale Lille Institut, 2020. English. ⟨NNT : 2020CLIL0002⟩
Thèse tel-03132893v1

Fast sampling from beta-ensembles

 Guillaume Gautier , Rémi Bardenet , Michal Valko
Statistics and Computing, 2021, 31 (7), ⟨10.1007/s11222-020-09984-0⟩
Article dans une revue hal-02697647v1
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Zonotope hit-and-run for efficient sampling from projection DPPs

 Guillaume Gautier , Rémi Bardenet , Michal Valko
International Conference on Machine Learning, 2017, Sydney, Australia
Communication dans un congrès hal-01526577v2
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On two ways to use determinantal point processes for Monte Carlo integration

 Guillaume Gautier , R. Bardenet , Michal Valko
NEGDEPML 2019 - ICML Workshop on Negative Dependence in ML, Jun 2019, Long Beach, CA, United States
Communication dans un congrès hal-02160382v1
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On two ways to use determinantal point processes for Monte Carlo integration -- Long version

 Guillaume Gautier , Rémi Bardenet , Michal Valko
NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Jun 2019, Vancouver, Canada
Communication dans un congrès hal-02277739v1
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