Sidestepping hardness in statistical problems
Abstract/Contents
- Abstract
- Recent increases of the availability of data have led to more demand for solving large-scale, computationally intensive statistical problems. Unfortunately, many such problems are provably hard and can require excessive amounts of data or computational resources. I will start out by showing an example hardness result and the implications the result has for practitioners. Keeping that in mind, we will discuss several approaches to sidestepping such hardness results. We will examine applications to the robotics problem of navigation among obstacles with estimated shape and location, the nonparametric statistics problem of log-concave density estimation and synthetically enlarging datasets.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Axelrod, Brian |
|---|---|
| Degree supervisor | Reingold, Omer |
| Degree supervisor | Valiant, Gregory |
| Thesis advisor | Reingold, Omer |
| Thesis advisor | Valiant, Gregory |
| Thesis advisor | Tan, Li-Yang |
| Degree committee member | Tan, Li-Yang |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Brian Axelrod. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/dj989fk4653 |
Access conditions
- Copyright
- © 2022 by Brian Axelrod
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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