{"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/neutra-lizing-bad-geometry-in-hamiltonian","title":"NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport","arxiv_id":"1903.03704","date":"2019-03-09","proceeding":null,"authors":["Matthew Hoffman","Pavel Sountsov","Joshua V. Dillon","Ian Langmore","Dustin Tran","Srinivas Vasudevan"],"abstract":"Hamiltonian Monte Carlo is a powerful algorithm for sampling from\ndifficult-to-normalize posterior distributions. However, when the geometry of\nthe posterior is unfavorable, it may take many expensive evaluations of the\ntarget distribution and its gradient to converge and mix. We propose neural\ntransport (NeuTra) HMC, a technique for learning to correct this sort of\nunfavorable geometry using inverse autoregressive flows (IAF), a powerful\nneural variational inference technique. The IAF is trained to minimize the KL\ndivergence from an isotropic Gaussian to the warped posterior, and then HMC\nsampling is performed in the warped space. We evaluate NeuTra HMC on a variety\nof synthetic and real problems, and find that it significantly outperforms\nvanilla HMC both in time to reach the stationary distribution and asymptotic\neffective-sample-size rates.","url_abs":"http://arxiv.org/abs/1903.03704v1","url_pdf":"http://arxiv.org/pdf/1903.03704v1.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":"neutra-lizing-bad-geometry-in-hamiltonian","repo_url":"https://github.com/google-research/google-research","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.03704","atlas_url":"https://app.syntology.ai/?focus=1903.03704","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}