Gaussian Process Bandits

Also known as GP

Latest papers

15 papers5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Gaussian Process Bandits with Paired Auxiliary Observations
Oct 17, 2026Weibo Huang, Qing Feng, Ruihao Zhu +1Bayesian Optimization Gaussian Process Bandits
Antai College of Economics and Management, Shanghai Jiao Tong University · School of Operations Research and Information Engineering, Cornell University · SC Johnson College of Business, Cornell University
Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence
Oct 14, 2026Yinan Li, Kwang-Sung Jun, Kapilan Balagopalan +4Gaussian Process Bandits Adaptive Sampling
University of Arizona · POSTECH CSE/GSAI · Meta Inc.
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback
Oct 13, 2026Shuang WuGaussian Process Bandits Multi-Armed Bandits
UCLA
Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Oct 12, 2026Hwanwoo Kim, Jungwoo Hong, Kyungjae LeeGaussian Process Bandits Bayesian Optimization
Department of Mathematical Sciences, University of Texas at Dallas · Department of Statistics, Korea University
Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers
Sep 23, 2026Sudarshan Manikantan, Abhishek BhattacharjeeGaussian Process Bandits Multi-Armed Bandits
Abstract Math Institute
Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration
Aug 19, 2026Matthias Mandl, Hanne KekkonenGaussian Process Bandits Bayesian Optimization
Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4, 2628 CD, Delft
Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
Aug 17, 2026Shion Takeno, Shogo IwazakiThompson Sampling Multi-Armed Bandits
Nagoya University · MI-6 Ltd.
Bellman-sufficient Information Complexity
Jun 9, 2026Yunbei XuMulti-Armed Bandits Minimax Regret
National University of Singapore
Regret Analysis of Retry-Based Bandits
May 20, 2026Bingkui Tong, Junpei Komiyama, Soichiro Nishimori +1Multi-Armed Bandits Gaussian Process Bandits
Mohamed bin Zayed University of Artificial Intelligence · RIKEN AIP · The University of Tokyo
Nearly Optimal Algorithms for Adversarial Kernelized Bandits
May 11, 2026Shogo IwazakiMulti-Armed Bandits Nyström Method
LY Corporation, Tokyo, Japan
Instantiating Bayesian CVaR lower bounds in Interactive Decision Making Problems
Apr 14, 2026Raghav Bongole, Tobias J. Oechtering, Mikael SkoglundGaussian Process Bandits Decision-Making under Uncertainty
Department of Information Science and Engineering (ISE), KTH Royal Institute of Technology
Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
Feb 20, 2026Shogo IwazakiGaussian Process Bandits Minimax Regret
LY Corporation, Tokyo, Japan
Fast Online Learning with Gaussian Prior-Driven Hierarchical Unimodal Thompson Sampling
Feb 17, 2026Tianchi Zhao, He Liu, Hongyin Shi +1Gaussian Process Bandits Multi-Armed Bandits
School of Intelligence Science and Technology, Beijing University Of Civil Engineering And Architecture, Beijing, 102616, China. · School of Economics and Management, Tsinghua University, Street, Beijing, 100084, China.
Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
Feb 16, 2026Somjit Roy, Prateek Jaiswal, Anirban Bhattacharya +2Gaussian Process Bandits Thompson Sampling
Department of Statistics, Texas A&M University, College Station, TX 77843, USA · Daniels School of Business, Purdue University, West Lafayette, IN 47907, USA · Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
No-Regret Gaussian Process Optimization of Time-Varying Functions
Nov 29, 2025Eliabelle Mauduit, Eloïse Berthier, Andrea SimonettoMulti-Armed Bandits Bayesian Optimization
Unit´e de Math´ematiques Appliqu´ees ENSTA, Institut Polytechnique de Paris Palaiseau, France · U2IS ENSTA, Institut Polytechnique de Paris Palaiseau, France

Oct 17, 2026J/K move · Enter open · S save

Gaussian Process Bandits with Paired Auxiliary Observations

Weibo Huang, Qing Feng, Ruihao Zhu, Cheng Hua

Antai College of Economics and Management, Shanghai Jiao Tong University · School of Operations Research and Information Engineering, Cornell University · SC Johnson College of Business, Cornell University

Bayesian Optimization Gaussian Process Bandits Variance Reduction

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Bayesian optimization (BO) is widely used for expensive black-box optimization, yet observation noise makes each evaluation less informative and slows posterior learning. Standard BO learns from costly outcomes gathered during optimization. In many applications, however, low-cost auxiliary data are available beforehand. We introduce Auxiliary-Informed Gaussian Process Bandit (AI-GPB), a variance-reduction method that combines an offline estimate of the auxiliary mean with a correction coefficient learned from paired online observations. Notably, it exploits correlation between paired observation errors without requiring the auxiliary mean to approximate the objective. We establish finite-sample GP confidence bounds and regret guarantees, including explicit cumulative and simple regret rates for Matérn kernels. Numerical experiments across continuous BO benchmarks with varied landscapes and dimensions show consistent reductions in cumulative and simple regret relative to vanilla GP-UCB, even when within-pair correlation is moderate and the auxiliary mean is estimated from noisy offline data.