Ilias Diakonikolas is an associate professor of computer science at the University of Wisconsin-Madison. His current research focuses on the algorithmic foundations of machine learning. Diakonikolas is a recipient of a number of research awards, including the best paper award at NeurIPS 2019. Daniel M. Kane is an associate professor at the University of California, San Diego in the departments of Computer Science and Mathematics. He is a four-time Putnam Fellow and two-time IMO gold medallist. Kane's research interests include number theory, combinatorics, computational complexity, and computational statistics.

'This is a timely book on efficient algorithms for computing robust statistics from noisy data. It presents lucid intuitive descriptions of the algorithms as well as precise statements of results with rigorous proofs - a nice combination indeed. The topic has seen fundamental breakthroughs over the last few years and the authors are among the leading contributors. The reader will get a ringside view of the developments.' Ravi Kannan, Visiting Professor, Indian Institute of Science

• 1. Introduction to robust statistics; 2. Efficient high-dimensional robust mean estimation; 3. Algorithmic refinements in robust mean estimation; 4. Robust covariance estimation; 5. List-decodable learning; 6. Robust estimation via higher moments; 7. Robust supervised learning; 8. Information-computation tradeoffs in high-dimensional robust statistics; A. Mathematical background; References; Index.

Ilias Diakonikolas is an associate professor of computer science at the University of Wisconsin-Madison. His current research focuses on the algorithmic foundations of machine learning. Diakonikolas is a recipient of a number of research awards, including the best paper award at NeurIPS 2019. Daniel M. Kane is an associate professor at the University of California, San Diego in the departments of Computer Science and Mathematics. He is a four-time Putnam Fellow and two-time IMO gold medallist. Kane's research interests include number theory, combinatorics, computational complexity, and computational statistics.

'This is a timely book on efficient algorithms for computing robust statistics from noisy data. It presents lucid intuitive descriptions of the algorithms as well as precise statements of results with rigorous proofs - a nice combination indeed. The topic has seen fundamental breakthroughs over the last few years and the authors are among the leading contributors. The reader will get a ringside view of the developments.' Ravi Kannan, Visiting Professor, Indian Institute of Science

• 1. Introduction to robust statistics; 2. Efficient high-dimensional robust mean estimation; 3. Algorithmic refinements in robust mean estimation; 4. Robust covariance estimation; 5. List-decodable learning; 6. Robust estimation via higher moments; 7. Robust supervised learning; 8. Information-computation tradeoffs in high-dimensional robust statistics; A. Mathematical background; References; Index.

Ilias Diakonikolas is an associate professor of computer science at the University of Wisconsin-Madison. His current research focuses on the algorithmic foundations of machine learning. Diakonikolas is a recipient of a number of research awards, including the best paper award at NeurIPS 2019. Daniel M. Kane is an associate professor at the University of California, San Diego in the departments of Computer Science and Mathematics. He is a four-time Putnam Fellow and two-time IMO gold medallist. Kane's research interests include number theory, combinatorics, computational complexity, and computational statistics.

'This is a timely book on efficient algorithms for computing robust statistics from noisy data. It presents lucid intuitive descriptions of the algorithms as well as precise statements of results with rigorous proofs - a nice combination indeed. The topic has seen fundamental breakthroughs over the last few years and the authors are among the leading contributors. The reader will get a ringside view of the developments.' Ravi Kannan, Visiting Professor, Indian Institute of Science

• 1. Introduction to robust statistics; 2. Efficient high-dimensional robust mean estimation; 3. Algorithmic refinements in robust mean estimation; 4. Robust covariance estimation; 5. List-decodable learning; 6. Robust estimation via higher moments; 7. Robust supervised learning; 8. Information-computation tradeoffs in high-dimensional robust statistics; A. Mathematical background; References; Index.