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1031 results in Computational statistics, machine learning and information science

Page 5 of 52

  • 1 - Introduction to Robust Statistics

  • Ilias Diakonikolas, University of Wisconsin-Madison, Daniel M. Kane, University of California, San Diego
  • Book:
    Algorithmic High-Dimensional Robust Statistics
    Published online:
    24 August 2023
    Print publication:
    07 September 2023, pp 1-28
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Algorithmic High-Dimensional Robust Statistics
  • Ilias Diakonikolas, Daniel M. Kane
  • Published online:
    24 August 2023
    Print publication:
    07 September 2023
  • 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.

  • 12 - Optimal Particle Filter

  • Daniel Sanz-Alonso, University of Chicago, Andrew Stuart, California Institute of Technology, Armeen Taeb, University of Washington
  • Book:
    Inverse Problems and Data Assimilation
    Published online:
    27 July 2023
    Print publication:
    10 August 2023, pp 153-163

  • Summary

    This chapter is devoted to the optimal particle filter (OPF). Like the bootstrap particle filter (BPF) from the previous chapter, the OPF approximates the filtering distribution by a sum of Dirac masses. But while the BPF is conceptually derived by factorizing the update of the filtering distribution into a prediction and an analysis step, the OPF uses a different factorization which can result in improved performance.

Page 5 of 52