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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.12815 |
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| _version_ | 1866914471783432192 |
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| author | Rásonyi, Miklós |
| author_facet | Rásonyi, Miklós |
| contents | The so-called SAGA-LD algorithm is used for efficient sampling from high-dimensional distributions in machine learning. Its intricate dynamics resists standard approaches of Markov chain theory. We prove, using a model-specific method, that SAGA-LD converges to a limiting distribution and a law of large numbers holds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12815 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On ergodicity of the SAGA-LD algorithm Rásonyi, Miklós Probability The so-called SAGA-LD algorithm is used for efficient sampling from high-dimensional distributions in machine learning. Its intricate dynamics resists standard approaches of Markov chain theory. We prove, using a model-specific method, that SAGA-LD converges to a limiting distribution and a law of large numbers holds. |
| title | On ergodicity of the SAGA-LD algorithm |
| topic | Probability |
| url | https://arxiv.org/abs/2604.12815 |