{"id":164090,"date":"2012-01-01T00:00:00","date_gmt":"2012-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/information-theoretic-lower-bounds-on-the-oracle-complexity-of-stochastic-convex-optimization\/"},"modified":"2018-10-16T20:06:44","modified_gmt":"2018-10-17T03:06:44","slug":"information-theoretic-lower-bounds-on-the-oracle-complexity-of-stochastic-convex-optimization","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/information-theoretic-lower-bounds-on-the-oracle-complexity-of-stochastic-convex-optimization\/","title":{"rendered":"Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization"},"content":{"rendered":"<p>Relative to the large literature on upper bounds<br \/>\non complexity of convex optimization, lesser attention has been<br \/>\npaid to the fundamental hardness of these problems. Given the<br \/>\nextensive use of convex optimization in machine learning and<br \/>\nstatistics, gaining an understanding of these complexity-theoretic<br \/>\nissues is important. In this paper, we study the complexity of<br \/>\nstochastic convex optimization in an oracle model of computation.<br \/>\nWe introduce a new notion of discrepancy between functions,<br \/>\nand use it to reduce problems of stochastic convex optimization<br \/>\nto statistical parameter estimation, which can be lower bounded<br \/>\nusing information-theoretic methods. Using this approach, we<br \/>\nimprove upon known results and obtain tight minimax complexity<br \/>\nestimates for various function classes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Given the extensive use of convex optimization in machine learning and statistics, gaining an understanding of these complexity-theoretic issues is important. In this paper, we study the complexity of stochastic [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"IEEE Transcations on Information Theory","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"IEEE Transcations on Information 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