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On Information Gain and Regret Bounds in Gaussian Process Bandits

International Conference on Artificial Intelligence and Statistics (AISTATS), 2020

15 September 2020

Papers citing "On Information Gain and Regret Bounds in Gaussian Process Bandits"

50 / 118 papers shown

Title
Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity Diego Martinez-Taboada Tomás González Aaditya Ramdas 0 0 0 05 Nov 2025
Bayesian Optimization on Networks Wenwen Li D. Sanz-Alonso Ruiyi Yang 20 0 0 31 Oct 2025
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes Jasmine Bayrooti Sattar Vakili Amanda Prorok Carl Henrik Ek 28 0 0 23 Oct 2025
Relative Information Gain and Gaussian Process Regression Hamish Flynn 64 0 0 05 Oct 2025
BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertainty Akshay Kudva J. Paulson 36 0 0 04 Oct 2025
Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization Manish Acharya David Hyde 92 0 0 22 Sep 2025
Safe Guaranteed Dynamics Exploration with Probabilistic Models Manish Prajapat Johannes Köhler Melanie Zeilinger Andreas Krause 40 0 0 20 Sep 2025
Generalized Kernelized Bandits: Self-Normalized Bernstein-Like Dimension-Free Inequality and Regret Bounds Alberto Maria Metelli Simone Drago Marco Mussi 32 2 0 03 Aug 2025
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization Shion Takeno Yu Inatsu Masayuki Karasuyama I. Takeuchi 117 0 0 13 Jul 2025
Bayesian Optimization over Bounded Domains with the Beta Product KernelConference on Uncertainty in Artificial Intelligence (UAI), 2025 Huy Hoang Nguyen Han Zhou Matthew B. Blaschko A. Tiulpin 101 0 0 19 Jun 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 113 2 0 13 Jun 2025
Safely Learning Controlled Stochastic Dynamics Luc Brogat-Motte Alessandro Rudi Riccardo Bonalli 100 0 0 03 Jun 2025
Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization Shogo Iwazaki GP 184 1 0 02 Jun 2025
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration Qinglin Zhu Runcong Zhao Hanqi Yan Yulan He Yudong Chen Lin Gui LRM 238 5 0 30 May 2025
Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds Aya Kayal Sattar Vakili Laura Toni Da-shan Shiu A. Bernacchia 120 0 0 29 May 2025
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization Chih-Yu Chang Milad Azvar Chinedum Okwudire Raed Al Kontar UQCV 171 2 0 20 May 2025
High-dimensional Nonparametric Contextual Bandit Problem Shogo Iwazaki Junpei Komiyama Masaaki Imaizumi 134 0 0 20 May 2025
Convergence Rates of Constrained Expected Improvement Haowei Wang Jingyi Wang Zhongxiang Dai Nai-Yuan Chiang Szu Hui Ng Cosmin G. Petra 133 0 0 16 May 2025
Dose-finding design based on level set estimation in phase I cancer clinical trials Keiichiro Seno Kota Matsui S. Iwazaki Yu Inatsu Shion Takeno Shigeyuki Matsui 81 0 0 12 Apr 2025
Wasserstein Distributionally Robust Bayesian Optimization with Continuous ContextInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025 F. Micheli Efe C. Balta Anastasios Tsiamis John Lygeros 135 1 0 26 Mar 2025
Sparse Nonparametric Contextual Bandits Hamish Flynn Julia Olkhovskaya Paul Rognon-Vael 219 0 0 20 Mar 2025
Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization Kevin Li Eric Laber 164 0 0 13 Mar 2025
Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits Shogo Iwazaki 152 1 0 26 Feb 2025
Distributionally Robust Active Learning for Gaussian Process Regression Shion Takeno Yoshito Okura Yu Inatsu Aoyama Tatsuya Tomonari Tanaka ... Noriaki Hashimoto Taro Murayama Hanju Lee Shinya Kojima Ichiro Takeuchi OOD GP 227 0 0 24 Feb 2025
Koopman-Equivariant Gaussian ProcessesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025 Nicolas Hoischen Max Beier Armin Lederer A. Capone Roland Toth Sandra Hirche AI4TS 170 4 0 10 Feb 2025
Differentially Private Kernelized Contextual BanditsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025 Nikola Pavlovic Sudeep Salgia Qing Zhao 151 3 0 13 Jan 2025
Vector Optimization with Gaussian Process Bandits İlter Onat Korkmaz Yaşar Cahit Yıldırım Çağın Ararat Cem Tekin 185 0 0 03 Dec 2024
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPsNeural Information Processing Systems (NeurIPS), 2024 Davide Maran Alberto Maria Metelli Matteo Papini Marcello Restelli 134 1 0 31 Oct 2024
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret AlgorithmNeural Information Processing Systems (NeurIPS), 2024 Sattar Vakili Julia Olkhovskaya 128 1 0 30 Oct 2024
Sample-efficient Bayesian Optimisation Using Known InvariancesNeural Information Processing Systems (NeurIPS), 2024 Theodore Brown Alexandru Cioba Ilija Bogunovic 110 5 0 22 Oct 2024
Lower Bounds for Time-Varying Kernelized BanditsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 Xu Cai Jonathan Scarlett 151 1 0 22 Oct 2024
Principled Bayesian Optimisation in Collaboration with Human Experts Wenjie Xu Masaki Adachi Colin N. Jones Michael A. Osborne 165 5 0 14 Oct 2024
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to OptimalNeural Information Processing Systems (NeurIPS), 2024 Juliusz Ziomek Masaki Adachi Michael A. Osborne 218 4 0 14 Oct 2024
Variational Search DistributionsInternational Conference on Learning Representations (ICLR), 2024 Daniel M. Steinberg Rafael Oliveira Cheng Soon Ong Edwin V. Bonilla 355 5 0 10 Sep 2024
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound Shion Takeno Yu Inatsu Masayuki Karasuyama 186 0 0 02 Sep 2024
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning Yen-Ru Lai Fu-Chieh Chang Pei-Yuan Wu OffRL 264 1 0 22 Aug 2024
A Contextual Combinatorial Bandit Approach to Negotiation Yexin Li Zhancun Mu Siyuan Qi 130 2 0 30 Jun 2024
Bandits with Preference Feedback: A Stackelberg Game Perspective Barna Pásztor Parnian Kassraie Andreas Krause 134 5 0 24 Jun 2024
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints Arpan Losalka Jonathan Scarlett 125 2 0 05 Jun 2024
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL Lenart Treven Bhavya Sukhija Yarden As Florian Dorfler Andreas Krause 236 6 0 03 Jun 2024
NeoRL: Efficient Exploration for Nonepisodic RL Bhavya Sukhija Lenart Treven Florian Dorfler Stelian Coros Andreas Krause OffRL 302 5 0 03 Jun 2024
$ε$ -Policy Gradient for Online Pricing Lukasz Szpruch Tanut Treetanthiploet Yufei Zhang OffRL 130 1 0 06 May 2024
Tighter Confidence Bounds for Sequential Kernel Regression H. Flynn David Reeb 124 5 0 19 Mar 2024
An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization Shouri Hu Jiawei Li Zhibo Cai 113 2 0 08 Mar 2024
TS-RSR: A provably efficient approach for batch Bayesian Optimization Tongzheng Ren Na Li 298 2 0 07 Mar 2024
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness Nikola Pavlovic Sudeep Salgia Qing Zhao 136 1 0 20 Feb 2024
Active Few-Shot Fine-Tuning Jonas Hübotter Bhavya Sukhija Lenart Treven Yarden As Andreas Krause 238 2 0 13 Feb 2024
Safe Guaranteed Exploration for Non-linear Systems Manish Prajapat Johannes Köhler M. Turchetta Andreas Krause Melanie Zeilinger 143 10 0 09 Feb 2024
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration Hwanwoo Kim D. Sanz-Alonso 204 6 0 30 Jan 2024
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian OptimizationAAAI Conference on Artificial Intelligence (AAAI), 2024 Xu Cai Jonathan Scarlett 123 0 0 11 Jan 2024