Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance

10 February 2025

Papers citing "Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance"

9 / 9 papers shown

Title
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits Shogo Iwazaki 58 1 0 26 Feb 2025
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency Sudeep Salgia Sattar Vakili Qing Zhao 38 9 0 23 Oct 2023
Optimal Order Simple Regret for Gaussian Process Bandits Sattar Vakili N. Bouziani Sepehr Jalali A. Bernacchia Da-shan Shiu 49 53 0 20 Aug 2021
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes Dongruo Zhou Quanquan Gu Csaba Szepesvári 56 205 0 15 Dec 2020
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance Sudeep Salgia Sattar Vakili Qing Zhao 55 34 0 27 Oct 2020
Bandit optimisation of functions in the Matérn kernel RKHS David Janz David R. Burt Javier I. González 21 43 0 28 Jan 2020
Tight Regret Bounds for Bayesian Optimization in One Dimension Jonathan Scarlett 90 27 0 30 May 2018
Finite-Time Analysis of Kernelised Contextual Bandits Michal Valko N. Korda Rémi Munos I. Flaounas N. Cristianini 101 271 0 26 Sep 2013
Convergence rates of efficient global optimization algorithms Adam D. Bull 87 641 0 18 Jan 2011