Perspectives on Stochastic Localization
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912829718659072 |
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| author | Shi, Bobby Tian, Kevin Zhang, Matthew S. |
| author_facet | Shi, Bobby Tian, Kevin Zhang, Matthew S. |
| contents | We survey different perspectives on the stochastic localization process of Eldan, a powerful construction that has had many exciting recent applications in high-dimensional probability and algorithm design. Unlike prior surveys on this topic, our focus is on giving a self-contained presentation of all known alternative constructions of Eldan's stochastic localization, with an emphasis on connections between different constructions. Our hope is that by collecting these perspectives, some of which had primarily arisen within a particular community (e.g., probability theory, theoretical computer science, information theory, or machine learning), we can broaden the accessibility of stochastic localization, and ease its future use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perspectives on Stochastic Localization Shi, Bobby Tian, Kevin Zhang, Matthew S. Probability Data Structures and Algorithms Machine Learning Statistics Theory We survey different perspectives on the stochastic localization process of Eldan, a powerful construction that has had many exciting recent applications in high-dimensional probability and algorithm design. Unlike prior surveys on this topic, our focus is on giving a self-contained presentation of all known alternative constructions of Eldan's stochastic localization, with an emphasis on connections between different constructions. Our hope is that by collecting these perspectives, some of which had primarily arisen within a particular community (e.g., probability theory, theoretical computer science, information theory, or machine learning), we can broaden the accessibility of stochastic localization, and ease its future use. |
| title | Perspectives on Stochastic Localization |
| topic | Probability Data Structures and Algorithms Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2510.04460 |