A computationally conscious search for interactions
Abstract/Contents
- Abstract
- We tackle the problem of variable selection with a focus on discovering interactions between variables. The number of potential interactions grows exponentially with the order of the interaction, making exhaustive search infeasible. We show that it is nonetheless possible to identify the variables involved in interactions (of any order) with only linear computation cost and in a nonparametric fashion. Our algorithm is based on minimizing a nonconvex objective, carefully designed to have a favorable landscape. We provide finite sample guarantees on both false positives (we show all stationary points of the objective exclude noise variables) and false negatives (we characterize the sample sizes needed for gradient descent to converge to a good stationary point).
Description
Type of resource | text |
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Form | electronic resource; remote; computer; online resource |
Extent | 1 online resource. |
Place | California |
Place | [Stanford, California] |
Publisher | [Stanford University] |
Copyright date | 2019; ©2019 |
Publication date | 2019; 2019 |
Issuance | monographic |
Language | English |
Creators/Contributors
Author | Liu, Keli, (Statistician) | |
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Degree supervisor | Tibshirani, Robert | |
Thesis advisor | Tibshirani, Robert | |
Thesis advisor | Owen, Art B | |
Thesis advisor | Wager, Stefan | |
Degree committee member | Owen, Art B | |
Degree committee member | Wager, Stefan | |
Associated with | Stanford University, Department of Statistics. |
Subjects
Genre | Theses |
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Genre | Text |
Bibliographic information
Statement of responsibility | Keli Liu. |
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Note | Submitted to the Department of Statistics. |
Thesis | Thesis Ph.D. Stanford University 2019. |
Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Keli Liu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 3.0 Unported license (CC BY-NC-SA).
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