Stochastic continuum-armed bandits with additive models: minimax regrets and adaptive algorithm
From MaRDI portal
Publication:2091834
DOI10.1214/22-AOS2182MaRDI QIDQ2091834
Publication date: 2 November 2022
Published in: The Annals of Statistics (Search for Journal in Brave)
self-similarityadaptivityminimax lower boundcurse of dimensionalityoptimal rate of convergenceregretadditive modelcommunication constraintsbandits
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Adaptive confidence intervals for regression functions under shape constraints
- Minimax optimal rates of estimation in high dimensional additive models
- Adaptive-treed bandits
- Optimal rates of convergence for covariance matrix estimation
- Fast learning rates for plug-in classifiers
- Asymptotically efficient adaptive allocation rules
- Additive regression and other nonparametric models
- An adaptation theory for nonparametric confidence intervals
- Adaptive confidence interval for pointwise curve estimation.
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- Confidence bands in density estimation
- Online linear optimization and adaptive routing
- Linearly Parameterized Bandits
- Playing Games with Approximation Algorithms
- Denumerable-Armed Bandits
- Learning Theory
- The Nonstochastic Multiarmed Bandit Problem
- 10.1162/153244303321897663
- A kernel method of estimating structured nonparametric regression based on marginal integration
- The Continuum-Armed Bandit Problem
- Regret and Convergence Bounds for a Class of Continuum-Armed Bandit Problems
- Bandit Algorithms
- MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
- Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates
- Bandits and Experts in Metric Spaces
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- Stochastic Convex Optimization with Bandit Feedback
- Minimax-optimal rates for sparse additive models over kernel classes via convex programming
- Improved Rates for the Stochastic Continuum-Armed Bandit Problem
- Online Learning with Prior Knowledge
- Some aspects of the sequential design of experiments
- Finite-time analysis of the multiarmed bandit problem
This page was built for publication: Stochastic continuum-armed bandits with additive models: minimax regrets and adaptive algorithm