On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization

20 August 2020

Papers citing "On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization"

11 / 11 papers shown

Title
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance S. Iwazaki Shion Takeno 78 1 0 10 Feb 2025
Lower Bounds for Time-Varying Kernelized Bandits Xu Cai Jonathan Scarlett 36 0 0 22 Oct 2024
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws Kush S. Bhatia Wenshuo Guo Jacob Steinhardt 27 0 0 23 Feb 2023
Global Optimization with Parametric Function Approximation Chong Liu Yu-Xiang Wang 36 7 0 16 Nov 2022
Instance-Dependent Regret Analysis of Kernelized Bandits S. Shekhar T. Javidi 27 3 0 12 Mar 2022
Nearly Optimal Algorithms for Level Set Estimation Blake Mason Romain Camilleri Subhojyoti Mukherjee Kevin G. Jamieson Robert D. Nowak Lalit P. Jain 30 22 0 02 Nov 2021
Optimal Order Simple Regret for Gaussian Process Bandits Sattar Vakili N. Bouziani Sepehr Jalali A. Bernacchia Da-shan Shiu 31 51 0 20 Aug 2021
Local Differential Privacy for Bayesian Optimization Xingyu Zhou Jian Tan 6 24 0 13 Oct 2020
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili Kia Khezeli Victor Picheny GP 25 126 0 15 Sep 2020
Max-value Entropy Search for Efficient Bayesian Optimization Zi Wang Stefanie Jegelka 110 403 0 06 Mar 2017
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic Jonathan Scarlett Andreas Krause V. Cevher 72 89 0 24 Oct 2016