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| Format: | Preprint |
| Published: |
2023
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| Online Access: | https://arxiv.org/abs/2305.10690 |
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| _version_ | 1866918133708619776 |
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| author | Montanari, Andrea |
| author_facet | Montanari, Andrea |
| contents | Diffusions are a successful technique to sample from high-dimensional distributions. The target distribution can be either explicitly given or learnt from a collection of samples. They implement a diffusion process whose endpoint is a sample from the target distribution. The drift of the diffusion process is typically represented as a neural network. Stochastic localization is a successful technique to prove mixing of Markov Chains and other functional inequalities in high dimension. An algorithmic version of stochastic localization was recently proposed in order to sample from certain statistical mechanics models.
This expository article has three objectives: $(i)$~Generalize the algorithmic construction to other stochastic localization processes. This construction is both simple and broadly applicable; $(ii)$~Clarify the connection between diffusions and stochastic localization. This allows to derive several known sampling schemes in a unified fashion; $(iii)$~Describe the insights that follow from this unified viewpoint. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10690 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sampling, Diffusions, and Stochastic Localization Montanari, Andrea Machine Learning Diffusions are a successful technique to sample from high-dimensional distributions. The target distribution can be either explicitly given or learnt from a collection of samples. They implement a diffusion process whose endpoint is a sample from the target distribution. The drift of the diffusion process is typically represented as a neural network. Stochastic localization is a successful technique to prove mixing of Markov Chains and other functional inequalities in high dimension. An algorithmic version of stochastic localization was recently proposed in order to sample from certain statistical mechanics models. This expository article has three objectives: $(i)$~Generalize the algorithmic construction to other stochastic localization processes. This construction is both simple and broadly applicable; $(ii)$~Clarify the connection between diffusions and stochastic localization. This allows to derive several known sampling schemes in a unified fashion; $(iii)$~Describe the insights that follow from this unified viewpoint. |
| title | Sampling, Diffusions, and Stochastic Localization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.10690 |