Robust learning : information theory and algorithms
Abstract/Contents
- Abstract
- We study the problem of robust learning in the presence of outliers when the dimensionality of the underlying space is large. We first develop a criterion, called resilience, under which robust learning is information-theoretically possible. We show that resilience gives tight bounds in many cases, and study its finite-sample behavior. Next, we turn our attention to efficient algorithms. We present two classes of algorithms---based on moment estimation and duality, respectively---that provide robust estimates as long as certain moments of the data are bounded. We apply these algorithms to mean estimation, stochastic optimization, and clustering.
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 | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Steinhardt, Jacob |
|---|---|
| Degree supervisor | Liang, Percy |
| Thesis advisor | Liang, Percy |
| Thesis advisor | Duchi, John |
| Thesis advisor | Valiant, Gregory |
| Degree committee member | Duchi, John |
| Degree committee member | Valiant, Gregory |
| Associated with | Stanford University, Computer Science Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jacob Steinhardt. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Jacob Noah Steinhardt
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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