{"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/efficient-first-order-algorithms-for-adaptive","title":"Efficient First-Order Algorithms for Adaptive Signal Denoising","arxiv_id":"1803.11262","date":"2018-03-29","proceeding":"ICML 2018 7","authors":["Dmitrii Ostrovskii","Zaid Harchaoui"],"abstract":"We consider the problem of discrete-time signal denoising, focusing on a\nspecific family of non-linear convolution-type estimators. Each such estimator\nis associated with a time-invariant filter which is obtained adaptively, by\nsolving a certain convex optimization problem. Adaptive convolution-type\nestimators were demonstrated to have favorable statistical properties. However,\nthe question of their computational complexity remains largely unexplored, and\nin fact we are not aware of any publicly available implementation of these\nestimators. Our first contribution is an efficient implementation of these\nestimators via some known first-order proximal algorithms. Our second\ncontribution is a computational complexity analysis of the proposed procedures,\nwhich takes into account their statistical nature and the related notion of\nstatistical accuracy. The proposed procedures and their analysis are\nillustrated on a simulated data benchmark.","url_abs":"http://arxiv.org/abs/1803.11262v3","url_pdf":"http://arxiv.org/pdf/1803.11262v3.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":"efficient-first-order-algorithms-for-adaptive","repo_url":"https://github.com/ostrodmit/AlgoRec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.11262","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}