Results 61 to 70 of about 4,126,170 (169)
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
CoRR, 2019 17 pages, 2 ...
Gi-Soo Kim, Myunghee Cho Paik
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Journal of the Royal Anthropological Institute, Volume 32, Issue 2, Page 554-574, June 2026.The contested dynamics of slum gentrification in Rio de Janeiro came into focus during the brief period of relative peace brought by the pacification policy leading up to the 2016 Olympics. In this unprecedented moment, Rio's South Zone favela residents experienced a respite from the daily confrontations with police operations and drug trade violence ...
Angela Torresan
wiley +1 more source
Communication Efficient Distributed Learning for Kernelized Contextual Bandits [PDF]
, 2022 We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distributed bandit learning, existing solutions are restricted to simple models ...
Wang, Huazheng +3 more
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In Situ Characterisation of Hydrogels via Dynamic Interface Printing
Advanced Science, Volume 13, Issue 28, 18 May 2026.ABSTRACT Hydrogels have become pivotal materials for tissue engineering, robotics, biomedical devices, and sensing applications due to their diverse material compositions and tunable mechanical properties. While significant effort has focused on developing novel manufacturing approaches such as extrusion bioprinting and light‐based fabrication methods,
Callum Vidler +2 more
wiley +1 more source
The epoch-greedy algorithm for contextual multi-armed bandits
, 2008 We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also known as bandits with side information). Epoch-Greedy has the following prop-erties: 1. No knowledge of a time horizon T is necessary. 2.
Tong Zhang, John Langford
core
Two-stage index computation for bandits with switching penalties II : switching delays [PDF]
This paper addresses the multi-armed bandit problem with switching penalties including both costs and delays, extending results of the companion paper [J. Niño-Mora.
Jose Nino-Mora
core
Adversarially robust stochastic multi-armed bandits
, 2020 Multi-armed bandits are a fundamental problem in sequential decision making with applications in optimal experimental design, online advertisement, recommender systems and much more.
Zimmert, Julian Ulf
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OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
CoRR, 2019 We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms that are designed solely for one of the regimes are known to be sub-optimal for the alternate regime.
Niladri S. Chatterji +2 more
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Linear Bayes policy for learning in contextual-bandits [PDF]
, 2013 Machine and Statistical Learning techniques are used in almost all online advertisement systems. The problem of discovering which content is more demanded (e.g. receive more clicks) can be modeled as a multi-armed bandit problem. Contextual bandits (i.e.,
Vargas Perez, Ana Maria +1 more
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Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits.
Zhang, Qin, Karpov, Nikolai
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