01957nam a22002777a 450000500170000000800410001702000180005804100080007608200210008410000170010524500620012225000100018426000500019430000160024452011670026065000230142765000300145065000210148065000220150165000350152365000160155865000250157465000410159965000150164065000240165520260717181017.0260717b        |||||||| |||| 00| 0 eng d  a9780262046985  aeng  a519.76 bT22 HAZ  aHazan, Elad 1 aIntroduction to online convex optimization / cElad Hazan  a2nd.   aEngland : bThe MIT Press Combridge, c2022.   axix, 222p.   aIn many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives.

Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features thoroughly updated material throughout; new chapters on boosting, adaptive regret, and approachability; expanded exposition on optimization; examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, and SVM training, offered throughout; and exercises that guide students in completing parts of proofs. 4aConvex programming 4aMathematical optimization 4aMachine learning 4aOnline algorithms 4aGradient methods (Mathematics) 4aGame theory 4aStatistical learning 4aArtificial intelligencexMathematics 4aAlgorithms 4aOperations research