{"list":"other","total":51,"rows":[{"id":"1704.00445","title":"On Kernelized Multi-armed Bandits","created":"2017-04-03","cited_by_count":352},{"id":"1301.2609","title":"Learning to Optimize Via Posterior Sampling","created":"2013-01-11","cited_by_count":256},{"id":"1807.02582","title":"Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences","created":"2018-07-06","cited_by_count":192},{"id":"1309.6869","title":"Finite-Time Analysis of Kernelised Contextual Bandits","created":"2013-09-26","cited_by_count":177},{"id":"1403.5556","title":"Learning to Optimize via Information-Directed Sampling","created":"2014-03-21","cited_by_count":168},{"id":"2009.06966","title":"On Information Gain and Regret Bounds in Gaussian Process Bandits","created":"2020-09-15","cited_by_count":158},{"id":"1401.5508","title":"Hilbert Space Methods for Reduced-Rank Gaussian Process Regression","created":"2014-01-21","cited_by_count":88},{"id":"1706.00090","title":"Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization","created":"2017-05-31","cited_by_count":78},{"id":"1810.10775","title":"Adversarially Robust Optimization with Gaussian Processes","created":"2018-10-25","cited_by_count":62},{"id":"2002.09038","title":"Distributionally Robust Bayesian Optimization","created":"2020-02-20","cited_by_count":61},{"id":"2110.07788","title":"Gaussian Process Bandit Optimization with Few Batches","created":"2021-10-15","cited_by_count":57},{"id":"2105.05806","title":"High-Dimensional Experimental Design and Kernel Bandits","created":"2021-05-12","cited_by_count":56},{"id":"2108.09262","title":"Optimal Order Simple Regret for Gaussian Process Bandits","created":"2021-08-20","cited_by_count":54},{"id":"2102.05858","title":"Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously","created":"2021-02-11","cited_by_count":48},{"id":"2010.13997","title":"A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance","created":"2020-10-27","cited_by_count":39},{"id":"2003.01971","title":"Corruption-Tolerant Gaussian Process Bandit Optimization","created":"2020-03-04","cited_by_count":34},{"id":"2008.08757","title":"On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization","created":"2020-08-20","cited_by_count":31},{"id":"2107.02371","title":"Weighted Gaussian Process Bandits for Non-stationary Environments","created":"2021-07-06","cited_by_count":30},{"id":"2004.11408","title":"Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming","created":"2020-04-23","cited_by_count":28},{"id":"1711.01037","title":"Sparsity, variance and curvature in multi-armed bandits","created":"2017-11-03","cited_by_count":24},{"id":"1805.11792","title":"Tight Regret Bounds for Bayesian Optimization in One Dimension","created":"2018-05-30","cited_by_count":20},{"id":"2105.06126","title":"Value-at-Risk Optimization with Gaussian Processes","created":"2021-05-13","cited_by_count":20},{"id":"2102.06296","title":"No-Regret Algorithms for Time-Varying Bayesian Optimization","created":"2021-02-11","cited_by_count":19},{"id":"1909.08540","title":"No-Regret Learning in Unknown Games with Correlated Payoffs","created":"2019-09-18","cited_by_count":18},{"id":"2007.05271","title":"Learning to Play Sequential Games versus Unknown Opponents","created":"2020-07-10","cited_by_count":18},{"id":"2008.08885","title":"No-regret Algorithms for Multi-task Bayesian Optimization","created":"2020-08-20","cited_by_count":18},{"id":"2009.08166","title":"Mean-Variance Analysis in Bayesian Optimization under Uncertainty","created":"2020-09-17","cited_by_count":18},{"id":"2111.01768","title":"Nearly Optimal Algorithms for Level Set Estimation","created":"2021-11-02","cited_by_count":17},{"id":"2205.14775","title":"An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge","created":"2022-05-29","cited_by_count":17},{"id":"2310.15351","title":"Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency","created":"2023-10-23","cited_by_count":17},{"id":"2005.04832","title":"Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS","created":"2020-05-11","cited_by_count":16},{"id":"2302.01511","title":"Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds","created":"2023-02-03","cited_by_count":16},{"id":"2502.06363","title":"Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance","created":"2025-02-10","cited_by_count":15},{"id":"2506.01393","title":"Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization","created":"2025-06-02","cited_by_count":13},{"id":"1802.09732","title":"Online learning with kernel losses","created":"2018-02-27","cited_by_count":12},{"id":"2102.05793","title":"Lenient Regret and Good-Action Identification in Gaussian Process Bandits","created":"2021-02-11","cited_by_count":12},{"id":"2002.05096","title":"Regret Bounds for Noise-Free Kernel-Based Bandits","created":"2020-02-12","cited_by_count":11},{"id":"2202.01850","title":"A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits","created":"2022-02-03","cited_by_count":11},{"id":"1803.10172","title":"Distributed Adaptive Sampling for Kernel Matrix Approximation","created":"2018-03-27","cited_by_count":9},{"id":"2502.19006","title":"Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits","created":"2025-02-26","cited_by_count":9},{"id":"1408.2368","title":"On the Complexity of Bandit Linear Optimization","created":"2014-08-11","cited_by_count":8},{"id":"2410.16052","title":"Near-Optimal Algorithm for Non-Stationary Kernelized Bandits","created":"2024-10-21","cited_by_count":8},{"id":"1908.10284","title":"Statistical and Computational Trade-Offs in Kernel K-Means","created":"2019-08-27","cited_by_count":7},{"id":"2010.12167","title":"Approximation Theory Based Methods for RKHS Bandits","created":"2020-10-23","cited_by_count":6},{"id":"2310.01609","title":"Adversarial Contextual Bandits Go Kernelized","created":"2023-10-02","cited_by_count":6},{"id":"2110.08449","title":"Adversarial Attacks on Gaussian Process Bandits","created":"2021-10-16","cited_by_count":5},{"id":"2203.06297","title":"Instance-Dependent Regret Analysis of Kernelized Bandits","created":"2022-03-12","cited_by_count":5},{"id":"2410.16692","title":"Lower Bounds for Time-Varying Kernelized Bandits","created":"2024-10-22","cited_by_count":5},{"id":"2602.17940","title":"Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere","created":"2026-02-20","cited_by_count":4},{"id":"2308.08291","title":"Robust Bayesian Satisficing","created":"2023-08-16","cited_by_count":3},{"id":"2603.09276","title":"On Regret Bounds of Thompson Sampling for Bayesian Optimization","created":"2026-03-10","cited_by_count":3}],"next_cursor":null}