Results 41 to 50 of about 497,803 (209)
Context-aware Multi-stakeholder Recommender Systems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system.
Tahereh Arabghalizi +1 more
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Model selection for contextual bandits
CoRR, 2019 We introduce the problem of model selection for contextual bandits, where a learner must adapt to the complexity of the optimal policy while balancing exploration and exploitation. Our main result is a new model selection guarantee for linear contextual bandits.
Dylan J. Foster +2 more
openaire +4 more sources
Federated Linear Contextual Bandits
CoRR, 2021 This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without ...
Ruiquan Huang +3 more
openaire +3 more sources
Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior
Transactions of the Association for Computational Linguistics, 2021 We study continual learning for natural language instruction generation, by observing human users’ instruction execution. We focus on a collaborative scenario, where the system both acts and delegates tasks to human users using natural language.
Noriyuki Kojima, Alane Suhr, Yoav Artzi
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Multi-Armed Bandits Convergence Acceleration Using Cluster Normalizations
IEEE AccessRecommender systems usually operate in non-stationary environments, so the standard techniques used to create recommendations begin to degrade after some use.
Victoria Strenadko +2 more
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Bandit Algorithms for Advertising Optimization: A Comparative Study [PDF]
ITM Web of ConferencesIn recent years, the rapid development of digital advertising has challenged advertisers to make optimal choices among multiple options quickly. This is crucial for increasing user engagement and return on investment.
Tian Ziyue
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British Educational Research Journal, EarlyView.Abstract The global teacher shortage continues to intensify, with disparate impacts across geographic and socio‐economic communities. In Queensland, Australia, where this study originates, post‐COVID teacher shortages have intensified workforce pressures, leaving several regional, rural and remote schools as some of the ‘hardest‐to‐staff’ in the ...
Matthew Readette +5 more
wiley +1 more source
State‐Dependent Market (In)Efficiency in Cryptocurrency Markets
Journal of Futures Markets, EarlyView.ABSTRACT This paper develops a meta‐learning framework to test whether cryptocurrency market inefficiency is static or state‐dependent. To resolve competing claims between the Efficient and Adaptive Market Hypotheses (AMH), we construct an adaptive trading strategy using high‐frequency data for 50 major cryptocurrencies.
Sasan Barak +2 more
wiley +1 more source
Discover ComputingIn multi-category retail settings, demand interactions across product categories make it difficult for isolated single-category pricing strategies to achieve system-level optimization.
Jun Liu
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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
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