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2002.05096
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Regret Bounds for Noise-Free Kernel-Based Bandits
12 February 2020
Sattar Vakili
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Papers citing
"Regret Bounds for Noise-Free Kernel-Based Bandits"
7 / 7 papers shown
Convergence Rates of Constrained Expected Improvement
Haowei Wang
Jingyi Wang
Zhongxiang Dai
Nai-Yuan Chiang
Szu Hui Ng
Cosmin G. Petra
367
2
0
16 May 2025
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
Shogo Iwazaki
282
1
0
26 Feb 2025
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
S. Iwazaki
Shion Takeno
392
9
0
10 Feb 2025
Tighter Confidence Bounds for Sequential Kernel Regression
H. Flynn
David Reeb
293
7
0
19 Mar 2024
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
Hwanwoo Kim
D. Sanz-Alonso
374
7
0
30 Jan 2024
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
International Conference on Machine Learning (ICML), 2023
Sudeep Salgia
Sattar Vakili
Qing Zhao
452
13
0
23 Oct 2023
Regret Bounds for Noise-Free Cascaded Kernelized Bandits
Zihan Li
Jonathan Scarlett
256
3
0
10 Nov 2022
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