Perturbations, Optimization, and Statistics - Paperback
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Availability:In StockContributor:Tamir Hazan (Editor), George Papandreou (Editor), Daniel Tarlow (Editor)Series:Neural Information ProcessingAudience:Young AdultPublish date:2023-12-05Pages:412
Language:EnglishPublisher:MIT PressISBN-13:9780262549943ISBN-10:262549948UPC:9780262549943Book Category:Computers, MathematicsBook Subcategory:Artificial Intelligence, Computer Science, Differential EquationsSize:10.00 x 8.00 x 0.84 inchesWeight:1.7924Product ID:SC44NAYSGB
A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of...
Language:EnglishPublisher:MIT PressISBN-13:9780262549943ISBN-10:262549948UPC:9780262549943Book Category:Computers, MathematicsBook Subcategory:Artificial Intelligence, Computer Science, Differential EquationsSize:10.00 x 8.00 x 0.84 inchesWeight:1.7924Product ID:SC44NAYSGB
Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology. George Papandreou is a Research Scientist for Google, Inc. Daniel Tarlow is a Researcher at Microsoft Research Cambridge, UK.
Publisher: MIT Press
Contributor(s)
Tamir Hazan (Editor), George Papandreou (Editor), Daniel Tarlow (Editor)
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