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03176aam a2200349 i 4500 001 705CB6CC440211EF98CC15ED37ECA4DB 003 SILO 005 20240717010108 008 230617s2023 enk b 001 0 eng 010 $a 2023024336 020 $a 1108837816 020 $a 9781108837811 035 $a (OCoLC)1370486984 040 $a LBSOR $b eng $e rda $c DLC $d UKMGB $d OCLCF $d YDX $d PAU $d OCLCO $d HF9 $d SILO 042 $a pcc 050 00 $a QA276.4 D53 2023 100 1 $a Diakonikolas, Ilias, $e author. 245 10 $a Algorithmic high-dimensional robust statistics / $c Ilias Diakonikolas, Daniel M. Kane. 264 1 $a Cambridge, United Kingdom ; $b Cambridge University Press, $c 2023. 300 $a xvi, 283 pages ; $c 24 cm 504 $a Includes bibliographical references and index. 505 0 $a Introduction to robust statistics -- Efficient high-dimensional robust mean estimation -- Algorithmic refinements in robust mean estimation -- Robust covariance estimation -- List-decodable learning -- Robust estimation via higher moments -- Robust supervised learning -- Information-computation trade-offs in high-dimensional robust statistics. 520 $a "This reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptions, including robustness to model misspecification and to adversarial outliers in the dataset"-- $c Provided by publisher. 520 8 $a 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. -- Provided by publisher. 650 0 $a Robust statistics $x Data processing. 650 0 $a Computer algorithms. 700 1 $a Kane, Daniel M., $d 1986- $e author. 776 08 $i Online version: $a Diakonikolas, Ilias. $t Algorithmic high-dimensional robust statistics $d Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2023 $z 9781108943161 $w (DLC) 2023024337 941 $a 1 952 $l USUX851 $d 20240717030829.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=705CB6CC440211EF98CC15ED37ECA4DB 994 $a C0 $b IWAInitiate Another SILO Locator Search