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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.10871 |
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| _version_ | 1866910614933209088 |
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| author | Sire, Charlie Richet, Yann Riche, Rodolphe Le Rullière, Didier Rohmer, Jérémy Pheulpin, Lucie |
| author_facet | Sire, Charlie Richet, Yann Riche, Rodolphe Le Rullière, Didier Rohmer, Jérémy Pheulpin, Lucie |
| contents | Quantization summarizes continuous distributions by calculating a discrete approximation. Among the widely adopted methods for data quantization is Lloyd's algorithm, which partitions the space into Voronoï cells, that can be seen as clusters, and constructs a discrete distribution based on their centroids and probabilistic masses. Lloyd's algorithm estimates the optimal centroids in a minimal expected distance sense, but this approach poses significant challenges in scenarios where data evaluation is costly, and relates to rare events. Then, the single cluster associated to no event takes the majority of the probability mass. In this context, a metamodel is required and adapted sampling methods are necessary to increase the precision of the computations on the rare clusters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_10871 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations Sire, Charlie Richet, Yann Riche, Rodolphe Le Rullière, Didier Rohmer, Jérémy Pheulpin, Lucie Computation Machine Learning Quantization summarizes continuous distributions by calculating a discrete approximation. Among the widely adopted methods for data quantization is Lloyd's algorithm, which partitions the space into Voronoï cells, that can be seen as clusters, and constructs a discrete distribution based on their centroids and probabilistic masses. Lloyd's algorithm estimates the optimal centroids in a minimal expected distance sense, but this approach poses significant challenges in scenarios where data evaluation is costly, and relates to rare events. Then, the single cluster associated to no event takes the majority of the probability mass. In this context, a metamodel is required and adapted sampling methods are necessary to increase the precision of the computations on the rare clusters. |
| title | FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2308.10871 |