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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
Conference papers
hal-02277739v1
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On sampling determinantal point processes
Guillaume, Michel, Jean Gautier
Theses
tel-03132893v1
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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
Conference papers
hal-02160382v1
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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
Conference papers
hal-01526577v2
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DPPy: Sampling Determinantal Point Processes with Python
Guillaume Gautier
,
Rémi Bardenet
,
Michal Valko
Journal of Machine Learning Research, 2019
Journal articles
hal-01879424v1
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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 articles
hal-02453683v1
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Fast sampling from beta-ensembles
Guillaume Gautier
,
Rémi Bardenet
,
Michal Valko
Journal articles
hal-02697647v1
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