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dc.contributor.advisorPatrick Jaillet.en_US
dc.contributor.authorFlajolet, Arthuren_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2017-10-30T15:04:26Z
dc.date.available2017-10-30T15:04:26Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/112015
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 159-166).en_US
dc.description.abstractThis thesis is concerned with the design and analysis of new algorithms for sequential optimization problems with limited feedback on the outcomes of alternatives when the environment is not perfectly known in advance and may react to past decisions. Depending on the setting, we take either a worst-case approach, which protects against a fully adversarial environment, or a hindsight approach, which adapts to the level of adversariality by measuring performance in terms of a quantity known as regret. First, we study stochastic shortest path problems with a deadline imposed at the destination when the objective is to minimize a risk function of the lateness. To capture distributional ambiguity, we assume that the arc travel times are only known through confidence intervals on some statistics and we design efficient algorithms minimizing the worst-case risk function. Second, we study the minimax achievable regret in the online convex optimization framework when the loss function is piecewise linear. We show that the curvature of the decision maker's decision set has a major impact on the growth rate of the minimax regret with respect to the time horizon. Specifically, the rate is always square root when the set is a polyhedron while it can be logarithmic when the set is strongly curved. Third, we study the Bandits with Knapsacks framework, a recent extension to the standard Multi-Armed Bandit framework capturing resource consumption. We extend the methodology developed for the original problem and design algorithms with regret bounds that are logarithmic in the initial endowments of resources in several important cases that cover many practical applications such as bid optimization in online advertising auctions. Fourth, we study more specifically the problem of repeated bidding in online advertising auctions when some side information (e.g. browser cookies) is available ahead of submitting a bid. Optimizing the bids is modeled as a contextual Bandits with Knapsacks problem with a continuum of arms. We design efficient algorithms with regret bounds that scale as square root of the initial budget.en_US
dc.description.statementofresponsibilityby Arthur Flajolet.en_US
dc.format.extent313 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleAdaptive optimization problems under uncertainty with limited feedbacken_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1006889672en_US


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