{"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/stochastic-gradient-descent-methods-for","title":"Stochastic gradient descent methods for estimation with large data sets","arxiv_id":"1509.06459","date":"2015-09-22","proceeding":null,"authors":["Dustin Tran","Panos Toulis","Edoardo M. Airoldi"],"abstract":"We develop methods for parameter estimation in settings with large-scale data\nsets, where traditional methods are no longer tenable. Our methods rely on\nstochastic approximations, which are computationally efficient as they maintain\none iterate as a parameter estimate, and successively update that iterate based\non a single data point. When the update is based on a noisy gradient, the\nstochastic approximation is known as standard stochastic gradient descent,\nwhich has been fundamental in modern applications with large data sets.\nAdditionally, our methods are numerically stable because they employ implicit\nupdates of the iterates. Intuitively, an implicit update is a shrinked version\nof a standard one, where the shrinkage factor depends on the observed Fisher\ninformation at the corresponding data point. This shrinkage prevents numerical\ndivergence of the iterates, which can be caused either by excess noise or\noutliers. Our sgd package in R offers the most extensive and robust\nimplementation of stochastic gradient descent methods. We demonstrate that sgd\ndominates alternative software in runtime for several estimation problems with\nmassive data sets. Our applications include the wide class of generalized\nlinear models as well as M-estimation for robust regression.","url_abs":"http://arxiv.org/abs/1509.06459v1","url_pdf":"http://arxiv.org/pdf/1509.06459v1.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":"stochastic-gradient-descent-methods-for","repo_url":"https://github.com/airoldilab/sgd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[{"method_slug":"sgd","method_name":"SGD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}