Decision Making And Inference Under Limited Information And High Dimensionality
dc.contributor.author | Ermon, Stefano | en_US |
dc.contributor.chair | Gomes, Carla P | en_US |
dc.contributor.committeeMember | Hopcroft, John E | en_US |
dc.contributor.committeeMember | Selman, Bart | en_US |
dc.date.accessioned | 2015-04-06T20:14:10Z | |
dc.date.available | 2020-01-27T07:00:30Z | |
dc.date.issued | 2015-01-26 | en_US |
dc.description.abstract | Statistical 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.identifier.other | bibid: 9154512 | |
dc.identifier.uri | https://hdl.handle.net/1813/39408 | |
dc.language.iso | en_US | en_US |
dc.title | Decision Making And Inference Under Limited Information And High Dimensionality | en_US |
dc.type | dissertation or thesis | en_US |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Cornell University | en_US |
thesis.degree.level | Doctor of Philosophy | |
thesis.degree.name | Ph. D., Computer Science |
Files
Original bundle
1 - 1 of 1