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2502.06363
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Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
10 February 2025
S. Iwazaki
Shion Takeno
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Papers citing
"Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance"
32 / 32 papers shown
No-Regret Gaussian Process Optimization of Time-Varying Functions
Eliabelle Mauduit
Eloïse Berthier
Andrea Simonetto
122
0
0
29 Nov 2025
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
Shion Takeno
Yu Inatsu
Masayuki Karasuyama
I. Takeuchi
366
3
0
13 Jul 2025
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
Hwanwoo Kim
Chong Liu
Yuxin Chen
394
4
0
13 Jun 2025
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
Shogo Iwazaki
GP
343
9
0
02 Jun 2025
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
Shogo Iwazaki
282
5
0
26 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
373
7
0
30 Jan 2024
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
International Conference on Machine Learning (ICML), 2023
Shion Takeno
Yu Inatsu
Masayuki Karasuyama
Ichiro Takeuchi
483
13
0
07 Nov 2023
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
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
Annual Conference Computational Learning Theory (COLT), 2023
Heyang Zhao
Jiafan He
Dongruo Zhou
Tong Zhang
Quanquan Gu
310
39
0
21 Feb 2023
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
International Conference on Machine Learning (ICML), 2023
Shion Takeno
Yu Inatsu
Masayuki Karasuyama
320
24
0
03 Feb 2023
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
Neural Information Processing Systems (NeurIPS), 2022
Dongruo Zhou
Quanquan Gu
293
57
0
23 May 2022
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
Neural Information Processing Systems (NeurIPS), 2021
Yeoneung Kim
Insoon Yang
Kwang-Sung Jun
267
45
0
05 Nov 2021
Gaussian Process Bandit Optimization with Few Batches
Zihan Li
Jonathan Scarlett
GP
556
58
0
15 Oct 2021
Optimal Order Simple Regret for Gaussian Process Bandits
Sattar Vakili
N. Bouziani
Sepehr Jalali
A. Bernacchia
Da-shan Shiu
268
61
0
20 Aug 2021
High-Dimensional Experimental Design and Kernel Bandits
International Conference on Machine Learning (ICML), 2021
Romain Camilleri
Julian Katz-Samuels
Kevin Jamieson
281
62
0
12 May 2021
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Neural Information Processing Systems (NeurIPS), 2021
Zihan Zhang
Jiaqi Yang
Xiangyang Ji
S. Du
440
49
0
29 Jan 2021
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Annual Conference Computational Learning Theory (COLT), 2020
Dongruo Zhou
Quanquan Gu
Csaba Szepesvári
334
228
0
15 Dec 2020
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
Neural Information Processing Systems (NeurIPS), 2020
Sudeep Salgia
Sattar Vakili
Qing Zhao
585
42
0
27 Oct 2020
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization
Xu Cai
Jonathan Scarlett
398
32
0
20 Aug 2020
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS
S. Shekhar
T. Javidi
221
23
0
11 May 2020
Regret Bounds for Noise-Free Kernel-Based Bandits
Sattar Vakili
394
8
0
12 Feb 2020
Bandit optimisation of functions in the Matérn kernel RKHS
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
David Janz
David R. Burt
Javier I. González
393
48
0
28 Jan 2020
Efficient Batch Black-box Optimization with Deterministic Regret Bounds
Yueming Lyu
Yuan. Yuan
Ivor W. Tsang
329
14
0
24 May 2019
Tight Regret Bounds for Bayesian Optimization in One Dimension
Jonathan Scarlett
544
34
0
30 May 2018
Information Directed Sampling and Bandits with Heteroscedastic Noise
Johannes Kirschner
Andreas Krause
404
129
0
29 Jan 2018
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
Annual Conference Computational Learning Theory (COLT), 2017
Jonathan Scarlett
Ilija Bogunovic
Volkan Cevher
435
121
0
31 May 2017
Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics
Felix Berkenkamp
Andreas Krause
Angela P. Schoellig
695
336
0
14 Feb 2016
Finite-Time Analysis of Kernelised Contextual Bandits
Conference on Uncertainty in Artificial Intelligence (UAI), 2013
Michal Valko
N. Korda
Rémi Munos
I. Flaounas
N. Cristianini
511
310
0
26 Sep 2013
Learning to Optimize Via Posterior Sampling
Mathematics of Operations Research (MOR), 2013
Daniel Russo
Benjamin Van Roy
864
751
0
11 Jan 2013
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
International Conference on Machine Learning (ICML), 2012
Nando de Freitas
Alex Smola
M. Zoghi
341
114
0
27 Jun 2012
Convergence rates of efficient global optimization algorithms
Adam D. Bull
910
699
0
18 Jan 2011
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