On Martingale Extensions of Vapnik–Chervonenkis Theory with Applications to Online Learning

DOI10.1007/978-3-319-21852-6_15zbMath1357.68181OpenAlexW2309498013MaRDI QIDQ2805727

Alexander Rakhlin, Karthik Sridharan

Publication date: 13 May 2016

Published in: Measures of Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-21852-6_15

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Martingales with discrete parameter (60G42) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Machine learning advised ski rental problem with a discount ⋮ Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case ⋮ Minimax rates for conditional density estimation via empirical entropy ⋮ Approachability, regret and calibration: implications and equivalences ⋮ Unnamed Item ⋮ Rademacher complexity for Markov chains: applications to kernel smoothing and Metropolis-Hastings ⋮ A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids

Cites Work

• Unnamed Item
• Unnamed Item
• Aggregation via empirical risk minimization
• Combinatorics of random processes and sections of convex bodies
• Learning by mirror averaging
• Martingales with values in uniformly convex spaces
• Empirical discrepancies and subadditive processes
• The weighted majority algorithm
• Efficient distribution-free learning of probabilistic concepts
• Entropy and the combinatorial dimension
• Weak convergence and empirical processes. With applications to statistics
• Fat-shattering and the learnability of real-valued functions
• On the Generalization Ability of On-Line Learning Algorithms
• Uniform Central Limit Theorems
• How to use expert advice
• Scale-sensitive dimensions, uniform convergence, and learnability
• The importance of convexity in learning with squared loss
• Prediction, Learning, and Games
• Convergence of stochastic processes

This page was built for publication: On Martingale Extensions of Vapnik–Chervonenkis Theory with Applications to Online Learning