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1. geurtsulgacbe Gilles Louppe glouppeulgacbe Department of Electrical Engineering and Computer Science GIGAR University of Liege Institut Monte64257ore Sart Tilman B28 B4000 Liege Belgium Editor Olivier Chapelle Yi Chang TieYan Liu Abstract In this pa 1 3718 25th Annual Conference on Learning Theory Exact Recovery of SparselyUsed Dictionaries Daniel A Spielman SPIELMAN CS YALE EDU Huan Wang HUAN WANG YALE EDU Department of Computer Science Yale University John Wright JOHNWRIGHT EE COLUMBIA EDU Dep kiritchenkonrccnrcgcca Institute for Information Technology National Research Council Canada Ottawa Canada Mikhail Jiline mzhilinepiphancom Epiphan Systems Inc Ottawa Canada Editor Saeys et al Abstract Sponsored search is a new application domain for 1 4223 25th Annual Conference on Learning Theory The Best of Both Worlds Stochastic and Adversarial Bandits ebastien Bubeck SBUBECK PRINCETON EDU Department of Operations Research and Financial Engineering Princeton University Princeton NJ USA Aleksa biggiodieeunicait Dept of Electrical and Electronic Engineering University of Cagliari Piazza dArmi 09123 Cagliari Italy and Blaine Nelson blainenelsonwsiiunituebingende Dept of Mathematics and Natural Sciences EberhardKarlsUniversitat Tubingen Sand berkeleyedu University of California Berkeley Division of Computer Science Peter L Bartlett bartlettcsberkeleyedu Univ of California Berkeley Division of Computer Science Department of Statistics Elad Hazan ehazanietechnionacil Technion Israel Insti com Bin Wu wubinbupteducn Bai Wang wangbaibupteducn Chuan Shi shichuanbupteducn Le Yu yulebuptgmailcom Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China Ed The goal is to minimize the number of tosses until we identify a coin whose posterior probability of being most biased is at least for a given Under a particular probabilistic model we give an optimal algorithm ie an algorithm that minimizes the ex 1 KeywordOptimizationinSponsoredSearchviaFeatureSelectionThecompanyisaccountableforcreatingshort-textadsofitsproductsorservicessuppliedwithalistofkeywords.Eachsearchadvertisingkeywordcanbeasinglewordora SupportedbyNSFgrantIIS-1118028.c 2015W.M.Koolen,M.K.Warmuth&D.Adamskiy. KOOLEN,WARMUTHANDADAMSKIYInput:DirectedgraphG=(V;E)withdesignatedsourceandsinknodes.Eachroundt=1;2;:::TheadversaryrevealsasetA Usman Roshan. CS 675. Comparison of classifiers. Empirical comparison of supervised classifiers – ICML 2006. Do we need hundreds of classifiers – JMLR 2014. Empirical comparison of supervised classifiers – ICML 2006 . LiXuJimmySJ.RenQiongYanYANQIONGSenseTimeGroupLimitedRenjieLiaoRJLIAO ProceedingsoftheInternationalConferenceonMachine,Lille,France,2015.JMLR:W&CPvolume37.Copy-right2015bytheauthor(s). DeepEdge-AwareFi Gueriftheadvantageofkeepingthesamplepointsinasubspaceoftheoriginalspace.Hence,thereduceddescriptionisdirectlyunderstandableanddoesnotrequireanyadditionalinter-pretationwork.Althoughfeatureselectionha