Branching Stein Variational Gradient Descent for sampling multimodal distributions
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912489006956544 |
|---|---|
| author | Bañales, Isaías Jaramillo, Arturo Ricalde-Guerrero, Joshué Helí |
| author_facet | Bañales, Isaías Jaramillo, Arturo Ricalde-Guerrero, Joshué Helí |
| contents | We propose a novel particle-based variational inference method designed to work with multimodal distributions. Our approach, referred to as Branched Stein Variational Gradient Descent (BSVGD), extends the classical Stein Variational Gradient Descent (SVGD) algorithm by incorporating a random branching mechanism that encourages the exploration of the state space. In this work, a theoretical guarantee for the convergence in distribution is presented, as well as numerical experiments to validate the suitability of our algorithm. Performance comparisons between the BSVGD and the SVGD are presented using the Wasserstein distance between samples and the corresponding computational times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Branching Stein Variational Gradient Descent for sampling multimodal distributions Bañales, Isaías Jaramillo, Arturo Ricalde-Guerrero, Joshué Helí Machine Learning Computation 62F15, 65C05, 65C35 We propose a novel particle-based variational inference method designed to work with multimodal distributions. Our approach, referred to as Branched Stein Variational Gradient Descent (BSVGD), extends the classical Stein Variational Gradient Descent (SVGD) algorithm by incorporating a random branching mechanism that encourages the exploration of the state space. In this work, a theoretical guarantee for the convergence in distribution is presented, as well as numerical experiments to validate the suitability of our algorithm. Performance comparisons between the BSVGD and the SVGD are presented using the Wasserstein distance between samples and the corresponding computational times. |
| title | Branching Stein Variational Gradient Descent for sampling multimodal distributions |
| topic | Machine Learning Computation 62F15, 65C05, 65C35 |
| url | https://arxiv.org/abs/2506.13916 |