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Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits [PDF]
, 2023 In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we present the first set of tradeoffs
Zhang, Qin, Karpov, Nikolai
core +1 more source
Variational inference for the multi-armed contextual bandit
CoRR, 2017 In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular, the contextual bandit case.
Iñigo Urteaga, Chris Wiggins 0001
openaire +4 more sources
Productization Challenges of Contextual Multi-Armed Bandits
CoRR, 2019 Contextual Multi-Armed Bandits is a well-known and accepted online optimization algorithm, that is used in many Web experiences to tailor content or presentation to users' traffic. Much has been published on theoretical guarantees (e.g. regret bounds) of proposed algorithmic variants, but relatively little attention has been devoted to the challenges ...
David Abensur +7 more
openaire +3 more sources
Contextual Multi-Armed Bandits for Causal Marketing
CoRR, 2018 This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action organically.
Neela Sawant +3 more
openaire +3 more sources
contextual: Evaluating Contextual Multi-Armed Bandit Problems in R
CoRR, 2018 55 pages, 12 ...
Robin van Emden, Maurits Kaptein
openaire +3 more sources
A Survey on Contextual Multi-armed Bandits
CoRR, 2015 In this survey we cover a few stochastic and adversarial contextual bandit algorithms. We analyze each algorithm's assumption and regret bound.
openaire +3 more sources
Selective Reviews of Bandit Problems in AI via a Statistical View
MathematicsReinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment.
Pengjie Zhou, Haoyu Wei, Huiming Zhang
doaj +1 more source
Respected and Ignored: British Women and the Arab Revolt (1936–39) in Mandate Palestine
Gender &History, EarlyView.ABSTRACT This article aims to reinsert British women and their perspectives into our understanding of anticolonial resistance in Palestine. Historiography on the Arab Revolt (1936–39) has previously been built from colonial state archives that disproportionally speak with a male voice and, as such, rely on certain gendered assumptions to describe the ...
Alex Worsfold
wiley +1 more source
Epsilon–First Policies for Budget–Limited Multi-Armed Bandits
, 2010 We introduce the budget–limited multi–armed bandit (MAB), which captures situations where a learner’s actions are costly and constrained by a fixed budget that is incommensurable with the rewards earned from the bandit machine, and then describe a first ...
Tran-Thanh, Long +4 more
core +2 more sources
MathematicsThe multi-armed bandit (MAB) problem is a foundational model for sequential decision-making under uncertainty. While MAB has proven valuable in applications such as clinical trials and online advertising, traditional formulations have limitations ...
Ali Baheri
doaj +1 more source