Bayesian Computation Meets Topology
DOKPE
Published in:
- Transactions on Machine Learning Research. - OpenReview. - 2024
English
Computational topology recently started to emerge as a novel paradigm for characterising
the ‘shape’ of high-dimensional data, leading to powerful algorithms in (un)supervised representation learning. While capable of capturing prominent features at multiple scales, topological methods cannot readily be used for Bayesian inference. We develop a novel
approach that bridges this gap, making it possible to perform parameter estimation in a
Bayesian framework, using topology-based loss functions. Our method affords easy integration into topological machine learning algorithms. We demonstrate its efficacy for parameter estimation in different simulation settings.
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Faculty
- Faculté des sciences et de médecine
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Department
- Département d'Informatique
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Language
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- Other electronic version
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Published on
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License
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CC BY
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Open access status
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gold
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Identifiers
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Persistent URL
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https://folia.unifr.ch/unifr/documents/330293
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