A computationally conscious search for interactions

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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
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)
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
Genre Text

Bibliographic information

Statement of responsibility Keli Liu.
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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