{"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/ds-mlr-exploiting-double-separability-for","title":"DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression","arxiv_id":"1604.04706","date":"2016-04-16","proceeding":null,"authors":["Parameswaran Raman","Sriram Srinivasan","Shin Matsushima","Xinhua Zhang","Hyokun Yun","S. V. N. Vishwanathan"],"abstract":"Scaling multinomial logistic regression to datasets with very large number of\ndata points and classes is challenging. This is primarily because one needs to\ncompute the log-partition function on every data point. This makes distributing\nthe computation hard. In this paper, we present a distributed stochastic\ngradient descent based optimization method (DS-MLR) for scaling up multinomial\nlogistic regression problems to massive scale datasets without hitting any\nstorage constraints on the data and model parameters. Our algorithm exploits\ndouble-separability, an attractive property that allows us to achieve both data\nas well as model parallelism simultaneously. In addition, we introduce a\nnon-blocking and asynchronous variant of our algorithm that avoids\nbulk-synchronization. We demonstrate the versatility of DS-MLR to various\nscenarios in data and model parallelism, through an extensive empirical study\nusing several real-world datasets. In particular, we demonstrate the\nscalability of DS-MLR by solving an extreme multi-class classification problem\non the Reddit dataset (159 GB data, 358 GB parameters) where, to the best of\nour knowledge, no other existing methods apply.","url_abs":"http://arxiv.org/abs/1604.04706v7","url_pdf":"http://arxiv.org/pdf/1604.04706v7.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":"ds-mlr-exploiting-double-separability-for","repo_url":"https://bitbucket.org/params/dsmlr","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"blocking","task_name":"Blocking"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}