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dc.contributor.authorErmon, Stefanoen_US
dc.date.accessioned2015-04-06T20:14:10Z
dc.date.available2020-01-27T07:00:30Z
dc.date.issued2015-01-26en_US
dc.identifier.otherbibid: 9154512
dc.identifier.urihttps://hdl.handle.net/1813/39408
dc.description.abstractStatistical inference in high-dimensional probabilistic models is one of the central problems of statistical machine learning and stochastic decision making. To date, only a handful of distinct methods have been developed, most notably (Markov Chain Monte Carlo) sampling, decomposition, and variational methods. In this dissertation, we will introduce a fundamentally new approach based on random projections and combinatorial optimization. Our approach provides provable guarantees on accuracy, and outperforms traditional methods in a range of domains, in particular those involving combinations of probabilistic and causal dependencies (such as those coming from physical laws) among the variables. This allows for a tighter integration between inductive and deductive reasoning, and offers a range of new modeling opportunities. As an example, we will discuss an application in the emerging field of Computational Sustainability aimed at discovering new fuel-cell materials where we greatly improved the quality of the results by incorporating prior background knowledge of the physics of the system into the model.en_US
dc.language.isoen_USen_US
dc.titleDecision Making And Inference Under Limited Information And High Dimensionalityen_US
dc.typedissertation or thesisen_US
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell Universityen_US
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Computer Science
dc.contributor.chairGomes, Carla Pen_US
dc.contributor.committeeMemberHopcroft, John Een_US
dc.contributor.committeeMemberSelman, Barten_US


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