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Information Directed Sampling and Bandits with Heteroscedastic Noise

29 January 2018

Papers citing "Information Directed Sampling and Bandits with Heteroscedastic Noise"

50 / 76 papers shown

Title
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 183 1 0 26 Mar 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 335 0 0 24 Feb 2025
Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance S. Iwazaki Shion Takeno 235 5 0 10 Feb 2025
Bayesian Optimization for Unknown Cost-Varying Variable Subsets with No-Regret CostsAAAI Conference on Artificial Intelligence (AAAI), 2024 Vu Viet Hoang Quoc Anh Hoang Nguyen Hung Tran The 185 0 0 20 Dec 2024
Second Order Bounds for Contextual Bandits with Function ApproximationInternational Conference on Learning Representations (ICLR), 2024 Aldo Pacchiano 542 7 0 24 Sep 2024
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits Junghyun Lee Se-Young Yun Kwang-Sung Jun 525 14 0 19 Jul 2024
Model-Free Active Exploration in Reinforcement Learning Alessio Russo Alexandre Proutiere OffRL 181 4 0 30 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 359 6 0 03 Jun 2024
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits Jie Bian Vincent Y. F. Tan FedML 180 2 0 24 May 2024
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning Qiaosheng Zhang Chenjia Bai Shuyue Hu Zhen Wang Xuelong Li 227 2 0 30 Apr 2024
Adaptive Bayesian Optimization for High-Precision Motion Systems Christopher König Raamadaas Krishnadas Efe C. Balta Alisa Rupenyan 138 2 0 22 Apr 2024
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits Zhiyong Wang Jize Xie Yi Chen J. C. Lui Dongruo Zhou 209 1 0 15 Mar 2024
Regret Minimization via Saddle Point OptimizationNeural Information Processing Systems (NeurIPS), 2024 Johannes Kirschner Seyed Alireza Bakhtiari Kushagra Chandak Volodymyr Tkachuk Csaba Szepesvári 144 2 0 15 Mar 2024
TS-RSR: A provably efficient approach for batch Bayesian Optimization Tongzheng Ren Na Li 338 2 0 07 Mar 2024
Optimistic Information Directed Sampling Gergely Neu Matteo Papini Ludovic Schwartz 249 3 0 23 Feb 2024
Linear bandits with polylogarithmic minimax regret Josep Lumbreras Marco Tomamichel 138 6 0 19 Feb 2024
Transition Constrained Bayesian Optimization via Markov Decision Processes Jose Pablo Folch Calvin Tsay Robert M. Lee B. Shafei Weronika Ormaniec Andreas Krause Mark van der Wilk Ruth Misener Mojmír Mutný 311 6 0 13 Feb 2024
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian OptimizationInternational Conference on Machine Learning (ICML), 2024 Kwang-Sung Jun Jungtaek Kim 170 4 0 12 Feb 2024
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments Jinyi Liu Zhi Wang Yan Zheng Jianye Hao Chenjia Bai Junjie Ye Zhen Wang Haiyin Piao Yang Sun 272 13 0 19 Dec 2023
Batch Bayesian Optimization for Replicable Experimental DesignNeural Information Processing Systems (NeurIPS), 2023 Zhongxiang Dai Q. Nguyen Sebastian Shenghong Tay Daisuke Urano Richalynn Leong Bryan Kian Hsiang Low Patrick Jaillet 154 6 0 02 Nov 2023
Efficient Exploration in Continuous-time Model-based Reinforcement LearningNeural Information Processing Systems (NeurIPS), 2023 Lenart Treven Jonas Hübotter Bhavya Sukhija Florian Dorfler Andreas Krause 203 16 0 30 Oct 2023
Experimental Designs for Heteroskedastic VarianceNeural Information Processing Systems (NeurIPS), 2023 Justin Weltz Tanner Fiez Alex Volfovsky Eric B. Laber Blake Mason Houssam Nassif Lalit P. Jain 240 8 0 06 Oct 2023
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale MixturesNeural Information Processing Systems (NeurIPS), 2023 H. Flynn David Reeb M. Kandemir Jan Peters 231 12 0 25 Sep 2023
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits Nicklas Werge Yi-Shan Wu Abdullah Akgul M. Kandemir 297 0 0 07 Jul 2023
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear BanditsInternational Conference on Learning Representations (ICLR), 2023 Yuwei Luo Mohsen Bayati 271 2 0 26 Jun 2023
Safe Risk-averse Bayesian Optimization for Controller TuningIEEE Robotics and Automation Letters (RA-L), 2023 Christopher Koenig Miks Ozols Anastasia Makarova Efe C. Balta Andreas Krause Alisa Rupenyan 88 11 0 23 Jun 2023
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret BoundsNeural Information Processing Systems (NeurIPS), 2023 Jiayi Huang Han Zhong Liwei Wang Lin F. Yang 270 11 0 12 Jun 2023
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards Xiang Li Qiang Sun 209 9 0 09 Mar 2023
Provably Efficient Reinforcement Learning via Surprise BoundInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Hanlin Zhu Ruosong Wang Jason D. Lee OffRL 147 5 0 22 Feb 2023
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational EfficiencyAnnual Conference Computational Learning Theory (COLT), 2023 Heyang Zhao Jiafan He Dongruo Zhou Tong Zhang Quanquan Gu 227 37 0 21 Feb 2023
Effective Dimension in Bandit Problems under CensorshipNeural Information Processing Systems (NeurIPS), 2023 G. Guinet Saurabh Amin Patrick Jaillet 139 2 0 14 Feb 2023
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and ApplicationsJournal of machine learning research (JMLR), 2023 Johannes Kirschner Tor Lattimore Andreas Krause 199 10 0 07 Feb 2023
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic BanditsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Subhojyoti Mukherjee Qiaomin Xie Josiah P. Hanna R. Nowak OffRL 268 5 0 29 Jan 2023
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input UncertaintyInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Yu Inatsu Shion Takeno Hiroyuki Hanada Kazuki Iwata Ichiro Takeuchi 122 4 0 27 Jan 2023
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision ProcessesInternational Conference on Machine Learning (ICML), 2022 Jiafan He Heyang Zhao Dongruo Zhou Quanquan Gu OffRL 345 62 0 12 Dec 2022
Movement Penalized Bayesian Optimization with Application to Wind Energy SystemsNeural Information Processing Systems (NeurIPS), 2022 Shyam Sundhar Ramesh Pier Giuseppe Sessa Andreas Krause Ilija Bogunovic 158 14 0 14 Oct 2022
Multi-Armed Bandits with Self-Information RewardsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022 Nir Weinberger M. Yemini 105 7 0 06 Sep 2022
Active Exploration via Experiment Design in Markov ChainsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022 Mojmír Mutný Tadeusz Janik Andreas Krause 190 19 0 29 Jun 2022
Nearly Minimax Optimal Reinforcement Learning with Linear Function ApproximationInternational Conference on Machine Learning (ICML), 2022 Pihe Hu Yu Chen Longbo Huang 279 36 0 23 Jun 2022
Regret Bounds for Information-Directed Reinforcement LearningNeural Information Processing Systems (NeurIPS), 2022 Botao Hao Tor Lattimore OffRL 206 23 0 09 Jun 2022
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual BanditsNeural Information Processing Systems (NeurIPS), 2022 Gergely Neu Julia Olkhovskaya Matteo Papini Ludovic Schwartz 260 20 0 27 May 2022
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPsNeural Information Processing Systems (NeurIPS), 2022 Dongruo Zhou Quanquan Gu 189 52 0 23 May 2022
Contextual Information-Directed SamplingInternational Conference on Machine Learning (ICML), 2022 Botao Hao Tor Lattimore Chao Qin 201 16 0 22 May 2022
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial CorruptionsNeural Information Processing Systems (NeurIPS), 2022 Jiafan He Dongruo Zhou Tong Zhang Quanquan Gu 205 53 0 13 May 2022
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium ComputationInternational Conference on Machine Learning (ICML), 2022 Pier Giuseppe Sessa Maryam Kamgarpour Andreas Krause 155 20 0 14 Mar 2022
Reward-Biased Maximum Likelihood Estimation for Neural Contextual BanditsAAAI Conference on Artificial Intelligence (AAAI), 2022 Yu-Heng Hung Ping-Chun Hsieh 193 2 0 08 Mar 2022
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic BanditsInternational Conference on Machine Learning (ICML), 2022 Heyang Zhao Dongruo Zhou Jiafan He Quanquan Gu 233 4 0 28 Feb 2022
Truncated LinUCB for Stochastic Linear Bandits Yanglei Song Meng zhou 452 0 0 23 Feb 2022
Bayesian Optimization for Distributionally Robust Chance-constrained ProblemInternational Conference on Machine Learning (ICML), 2022 Yu Inatsu Shion Takeno Masayuki Karasuyama Ichiro Takeuchi 156 13 0 31 Jan 2022
Bregman Deviations of Generic Exponential FamiliesAnnual Conference Computational Learning Theory (COLT), 2022 Sayak Ray Chowdhury Patrick Saux Odalric-Ambrym Maillard Aditya Gopalan 335 14 0 18 Jan 2022