Nonparametric Density Estimation: The L1 View

TL;DR: The book consists of three sections and provides a tutorial overview of the principles underlying data mining algorithms and their application, and shows how all of the preceding analysis fits together when applied to real-world data mining problems.

TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.

TL;DR: Main concepts and contributions to DRO are surveyed, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization are surveyed.

TL;DR: In this paper, the authors present a review of recent developments in nonparametric density estimation and include topics that have been omitted from review articles and books on the subject, such as the histogram, kernel estimators, and orthogonal series estimators.