Results 21 to 30 of about 3,144 (205)
Multi-armed quantum bandits: Exploration versus exploitation when learning properties of quantum states [PDF]
Quantum, 2022 We initiate the study of tradeoffs between exploration and exploitation in online learning of properties of quantum states. Given sequential oracle access to an unknown quantum state, in each round, we are tasked to choose an observable from a set of ...
Josep Lumbreras +2 more
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Multi-armed bandits with dependent arms
Machine Learning, 2023 We study a variant of the classical multi-armed bandit problem (MABP) which we call as Multi-Armed Bandits with dependent arms. More specifically, multiple arms are grouped together to form a cluster, and the reward distributions of arms belonging to the same cluster are known functions of an unknown parameter that is a characteristic of the cluster ...
Rahul Singh 0001 +3 more
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Multi-Armed Bandits and Quantum Channel Oracles [PDF]
QuantumMulti-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible ...
Simon Buchholz +2 more
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Satisficing in Multi-Armed Bandit Problems [PDF]
IEEE Transactions on Automatic Control, 2017 To appear in IEEE Transactions on Automatic ...
Paul Reverdy +2 more
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QoS-Aware Multi-armed Bandits [PDF]
2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2016 Motivated by runtime verification of QoS requirements in self-adaptive and self-organizing systems that are able to reconfigure their structure and behavior in response to runtime data, we propose a QoS-aware variant of Thompson sampling for multi-armed bandits.
Lenz Belzner, Thomas Gabor
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Budgeted Combinatorial Multi-Armed Bandits [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2022 We consider a budgeted combinatorial multi-armed bandit setting where, in every round, the algorithm selects a super-arm consisting of one or more arms. The goal is to minimize the total expected regret after all rounds within a limited budget. Existing techniques in this literature either fix the budget per round or fix the number of arms pulled in ...
Debojit Das +2 more
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Hedging using reinforcement learning: Contextual k-armed bandit versus Q-learning
Journal of Finance and Data Science, 2023 The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous replication, such as in the model of Black, Scholes and Merton (BSM), is ...
Loris Cannelli +3 more
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Multi-armed bandits for performance marketing
International Journal of Data Science and Analytics, 2023 Abstract This paper deals with the problem of optimising bids and budgets of a set of digital advertising campaigns. We improve on the current state of the art by introducing support for multi-ad group marketing campaigns and developing a highly data efficient parametric contextual bandit.
Gigli, M, Stella, F
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On Kernelized Multi-armed Bandits
CoRR, 2017 We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussian process-based algorithms for continuous bandit optimization-Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and derive corresponding regret bounds. Specifically,
Sayak Ray Chowdhury, Aditya Gopalan
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Correlation-Aware Collaborative Adaptive Window Algorithm for Multi-Armed Bandits [PDF]
ITM Web of ConferencesThe Multi-Armed Bandit (MAB) problem is central to reinforcement learning, where it addresses the trade-off between exploration and exploitation. However, traditional MAB algorithms often encounter difficulties in non-stationary environments with ...
Xu Yaoxin
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