{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-stein-variational-newton-method","title":"A Stein variational Newton method","arxiv_id":"1806.03085","date":"2018-06-08","proceeding":"NeurIPS 2018 12","authors":["Gianluca Detommaso","Tiangang Cui","Alessio Spantini","Youssef Marzouk","Robert Scheichl"],"abstract":"Stein variational gradient descent (SVGD) was recently proposed as a general\npurpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]:\nit minimizes the Kullback-Leibler divergence between the target distribution\nand its approximation by implementing a form of functional gradient descent on\na reproducing kernel Hilbert space. In this paper, we accelerate and generalize\nthe SVGD algorithm by including second-order information, thereby approximating\na Newton-like iteration in function space. We also show how second-order\ninformation can lead to more effective choices of kernel. We observe\nsignificant computational gains over the original SVGD algorithm in multiple\ntest cases.","url_abs":"http://arxiv.org/abs/1806.03085v2","url_pdf":"http://arxiv.org/pdf/1806.03085v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-stein-variational-newton-method","repo_url":"https://github.com/gianlucadetommaso/Stein-variational-samplers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-stein-variational-newton-method","repo_url":"https://github.com/klemens-floege/svn_ensembles","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.03085","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}