Description: Robust statistics is the study of designing estimators that perform well even when the dataset significantly deviates from the idealized modeling assumptions, such as in the presence of model misspecification or adversarial outliers in the dataset. The classical statistical theory, dating back to pioneering works by Tukey and Huber, characterizes the information-theoretic limits of robust estimation for most common problems. A recent line of work in computer science gave the first computationally efficient robust estimators in high dimensions for a range of learning tasks. This reference text for graduate students, researchers, and professionals in machine learning theory, provides an overview of recent developments in algorithmic high-dimensional robust statistics, presenting the underlying ideas in a clear and unified manner, while leveraging new perspectives on the developed techniques to provide streamlined proofs of these results. The most basic and illustrative results are analyzed in each chapter, while more tangential developments are explored in the exercises.
Price: 64.34 USD
Location: Hillsdale, NSW
End Time: 2024-11-30T11:43:01.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 60 Days
Refund will be given as: Money back or replacement (buyer's choice)
Return policy details:
EAN: 9781108837811
UPC: 9781108837811
ISBN: 9781108837811
MPN: N/A
Book Title: Algorithmic High-Dimensional Robust Statistics by
Item Weight: 0.58 kg
Number of Pages: 300 Pages
Language: English
Publication Name: Algorithmic High-Dimensional Robust Statistics
Publisher: Cambridge University Press
Publication Year: 2023
Item Height: 0.9 in
Subject: General, Computer Vision & Pattern Recognition
Type: Textbook
Item Length: 9.3 in
Author: Ilias Diakonikolas, Daniel M. Kane
Subject Area: Mathematics, Computers
Item Width: 6.2 in
Format: Hardcover
