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Book contents
- Frontmatter
- Contents
- Preface
- List of Contributors
- 1Introduction
- Part OneRefinements of Worst-Case Analysis
- Part TwoDeterministic Models of Data
- Part ThreeSemirandom Models
- Part FourSmoothed Analysis
- Part FiveApplications in Machine Learning and Statistics
- 16Noise in Classification
- 17Robust High-Dimensional Statistics
- 18Nearest Neighbor Classification and Search
- 19Efficient Tensor Decompositions
- 20Topic Models and Nonnegative Matrix Factorization
- 21Why Do Local Methods Solve Nonconvex Problems?
- 22Generalization in Overparameterized Models
- 23Instance Optimal Distribution Testing and Learning
- Part SixFurther Applications
- Index
23 - Instance Optimal Distribution Testing and Learning
fromPart Five - Applications in Machine Learning and Statistics
Published online by Cambridge University Press: 17 December 2020
- Tim Roughgarden
- Affiliation:Columbia University, New York
Book contents
- Frontmatter
- Contents
- Preface
- List of Contributors
- 1Introduction
- Part OneRefinements of Worst-Case Analysis
- Part TwoDeterministic Models of Data
- Part ThreeSemirandom Models
- Part FourSmoothed Analysis
- Part FiveApplications in Machine Learning and Statistics
- 16Noise in Classification
- 17Robust High-Dimensional Statistics
- 18Nearest Neighbor Classification and Search
- 19Efficient Tensor Decompositions
- 20Topic Models and Nonnegative Matrix Factorization
- 21Why Do Local Methods Solve Nonconvex Problems?
- 22Generalization in Overparameterized Models
- 23Instance Optimal Distribution Testing and Learning
- Part SixFurther Applications
- Index
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- Beyond the Worst-Case Analysis of Algorithms , pp. 506 - 526Publisher: Cambridge University PressPrint publication year: 2021