Results 11 to 20 of about 33,931 (272)
Online Multi-Armed Bandit [PDF]
, 2017 We introduce a novel variant of the multi-armed bandit problem, in which bandits are streamed one at a time to the player, and at each point, the player can either choose to pull the current bandit or move on to the next bandit. Once a player has moved on from a bandit, they may never visit it again, which is a crucial difference between our problem ...
Uma Roy, Ashwath Thirmulai, Joe Zurier
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Satisficing in multi-armed bandit problems [PDF]
IEEE Transactions on Automatic Control, 2016 Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty. We propose two sets of satisficing objectives for the multi-armed bandit problem, where the objective is to achieve reward-based decision ...
Leonard, Naomi Ehrich +2 more
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The Assistive Multi-Armed Bandit [PDF]
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019 Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences.
Chan, Lawrence +3 more
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Ballooning multi-armed bandits [PDF]
Artificial Intelligence, 2021 A full version of this paper is accepted in the Journal of Artificial Intelligence (AIJ) of Elsevier. A preliminary version is published as an extended abstract in AAMAS 2020.
Ganesh Ghalme +4 more
openaire +2 more sources
Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series [PDF]
Scientific Reports, 2020 Mikio Hasegawa +2 more
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach [PDF]
IEEE Access, 2023 Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data.
Dan Ben Ami, Kobi Cohen, Qing Zhao
semanticscholar +1 more source
Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges [PDF]
Statistical Science, 2015 Sofía S Villar, James M S Wason
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Episodic Multi-armed Bandits [PDF]
, 2015 We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, called the $stop$ action, that ends
Cem Tekin, Mihaela van der Schaar
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Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach [PDF]
IEEE Conference on Computer Communications, 2021 Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed ...
Xiong Wang, Jiancheng Ye, John C.S. Lui
semanticscholar +1 more source
Multi-Armed Bandit-Based Client Scheduling for Federated Learning [PDF]
IEEE Transactions on Wireless Communications, 2020 By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update
Wenchao Xia +5 more
semanticscholar +1 more source