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Causal contextual bandits with one-shot data integration [PDF]
Frontiers in Artificial IntelligenceWe study a contextual bandit setting where the agent has access to causal side information, in addition to the ability to perform multiple targeted experiments corresponding to potentially different context-action pairs—simultaneously in one-shot within ...
Chandrasekar Subramanian +3 more
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A Contextual-Bandit-Based Approach for Informed Decision-Making in Clinical Trials [PDF]
Life, 2022 Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner.
Yogatheesan Varatharajah, Brent Berry
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Multi-objective contextual bandits in recommendation systems for smart tourism [PDF]
Scientific ReportsIn the context of smart tourism, recommender systems play a pivotal role in enhancing the personalization and quality of travel experiences. Tourists often face challenges in decision-making due to information overload.
Sara Qassimi, Said Rakrak
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Conservative Contextual Combinatorial Cascading Bandit
IEEE Access, 2021 Contextual combinatorial cascading bandit ( $C^{3}$ -bandit) is a powerful multi-armed bandit framework that balances the tradeoff between exploration and exploitation in the learning process.
Kun Wang
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An embedded bandit algorithm based on agent evolution for cold-start problem
International Journal of Crowd Science, 2021 PurposeMany recommender systems are generally unable to provide accurate recommendations to users with limited interaction history, which is known as the cold-start problem. This issue can be resolved by trivial approaches that select random items or the
Rui Qiu, Wen Ji
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Achieving User-Side Fairness in Contextual Bandits
Human-Centric Intelligent Systems, 2022 Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback.
Wen Huang +4 more
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Future Internet, 2021 Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their ...
May Altulyan +4 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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Contextual Bandit Approach-based Recommendation System for Personalized Web-based Services
Applied Artificial Intelligence, 2021 In recent years, recommendation systems have started to gain significant attention and popularity. A recommendation system plays a significant role in various applications and services such as e-commerce, video streaming websites, etc.
Akshay Pilani +5 more
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StreamingBandit: Experimenting with Bandit Policies
Journal of Statistical Software, 2020 A large number of statistical decision problems in the social sciences and beyond can be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard to develop and evaluate policies that tackle these types of problems, and to use
Jules Kruijswijk +3 more
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