Extracting certainty from uncertainty: regret bounded by variation in costs

DOI10.1007/S10994-010-5175-XzbMath1470.68115OpenAlexW1964009380MaRDI QIDQ1959595

Satyen Kale, Elad Hazan

Publication date: 7 October 2010

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-010-5175-x

zbMATH Keywords

online learningprediction with expert adviceregret minimizationindividual sequences

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Online algorithms; streaming algorithms (68W27)

Related Items (6)

Lower bounds on individual sequence regret ⋮ Relaxing the i.i.d. assumption: adaptively minimax optimal regret via root-entropic regularization ⋮ Regret bounded by gradual variation for online convex optimization ⋮ Optimal learning with Bernstein Online Aggregation ⋮ AN ONLINE PORTFOLIO SELECTION ALGORITHM WITH REGRET LOGARITHMIC IN PRICE VARIATION ⋮ Unnamed Item

Uses Software

• AdaBoost.MH

Cites Work

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• Exponentiated gradient versus gradient descent for linear predictors
• A game of prediction with expert advice
• The weighted majority algorithm
• A decision-theoretic generalization of on-line learning and an application to boosting
• Improved second-order bounds for prediction with expert advice
• Efficient algorithms for online decision problems
• 10.1162/153244301753683726
• Universal Portfolios
• The Nonstochastic Multiarmed Bandit Problem
• Algorithmic Learning Theory
• Prediction, Learning, and Games

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