On ergodicity of the SAGA-LD algorithm
Fuente:
arXiv
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914471783432192 |
|---|---|
| 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 |