{"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/neo-non-equilibrium-sampling-on-the-orbits-of","title":"NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform","arxiv_id":null,"date":"2021-12-01","proceeding":"NeurIPS 2021 12","authors":["Achille Thin","Yazid Janati El Idrissi","Sylvain Le Corff","Charles Ollion","Eric Moulines","Arnaud Doucet","Alain Durmus","Christian Robert"],"abstract":"Sampling from a complex distribution $\\pi$ and approximating its intractable normalizing constant $\\mathrm{Z}$ are challenging problems. In this paper, a novel family of importance samplers (IS) and Markov chain Monte Carlo (MCMC) samplers is derived. Given an invertible map $\\mathrm{T}$, these schemes combine (with weights) elements from the forward and backward Orbits   through points sampled from a proposal distribution $\\rho$. The map $\\mathrm{T}$ does not leave the target $\\pi$ invariant, hence the name NEO, standing for Non-Equilibrium Orbits. NEO-IS provides unbiased estimators of the normalizing constant and self-normalized IS estimators of expectations under $\\pi$ while NEO-MCMC combines multiple NEO-IS estimates of the normalizing constant and an iterated sampling-importance resampling mechanism to sample from $\\pi$. For $\\mathrm{T}$ chosen as a discrete-time integrator of a conformal Hamiltonian system, NEO-IS achieves state-of-the art performance on difficult benchmarks and NEO-MCMC is able to explore highly multimodal targets. Additionally, we provide detailed theoretical results for both methods. In particular, we show that NEO-MCMC is uniformly geometrically ergodic and establish explicit mixing time estimates under mild conditions.","url_abs":"http://proceedings.neurips.cc/paper/2021/hash/8dd291cbea8f231982db0fb1716dfc55-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2021/file/8dd291cbea8f231982db0fb1716dfc55-Paper.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":"neo-non-equilibrium-sampling-on-the-orbits-of","repo_url":"https://github.com/Achillethin/NEO_non_equilibrium_sampling","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}