{"id":163762,"date":"2012-12-01T00:00:00","date_gmt":"2012-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/distributed-non-stochastic-experts\/"},"modified":"2018-11-09T01:25:42","modified_gmt":"2018-11-09T09:25:42","slug":"distributed-non-stochastic-experts","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/distributed-non-stochastic-experts\/","title":{"rendered":"Distributed Non-Stochastic Experts"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to <i>k<\/i> sites, and the sites are required to communicate with each other via the coordinator. At each time-step <i>t<\/i>, one of the <i>k<\/i> site nodes has to pick an expert from the set <i>{1, \u2026, n}<\/i>, and the same site receives information about payoffs of all experts for that round. The goal of the distributed system is to minimize regret at time horizon <i>T<\/i>, while simultaneously keeping communication to a minimum. The two extreme solutions to this problem are:<\/p>\n<ol>\n<li>Full communication: This essentially simulates the non-distributed setting to obtain the optimal <i>O(\u221alog (n)T)<\/i> regret bound at the cost of <i>T<\/i> communication.<\/li>\n<li>No communication: Each site runs an independent copy \u2013 the regret is <i>O(\u221alog (n)kT)<\/i> and the communication is <i>0<\/i>. This paper shows the difficulty of simultaneously achieving regret asymptotically better than <i>\u221akT<\/i> and communication better than <i>T<\/i>. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret <i>O(\u221ak<sup>5(1 + \u03b5)\/6<\/sup>T)<\/i> and communication <i>O(T\/k<sup>\u03b5<\/sup>)<\/i>, for any value of <i>\u03b5 \u2208 (0, 1\/5)<\/i>.<\/li>\n<\/ol>\n<p>We also consider a variant of the model, where the coordinator picks the expert. In this model, we show that the label-efficient forecaster of Cesa-Bianchi et al. (2005) already gives us strategy that is near optimal in regret vs communication trade-off.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to k sites, and the sites are required to communicate with each other via the coordinator. At each time-step t, one of the k site nodes has to pick an expert from the set {1, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-163762","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"Neural Information Processing Systems 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