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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Signal detection models as contextual bandits [PDF]
Royal Society Open Science, 2023 Signal detection theory (SDT) has been widely applied to identify the optimal discriminative decisions of receivers under uncertainty. However, the approach assumes that decision-makers immediately adopt the appropriate acceptance threshold, even though ...
Thomas N. Sherratt, Erica O'Neill
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Maximum Entropy Exploration in Contextual Bandits with Neural Networks and Energy Based Models [PDF]
Entropy, 2023 Contextual bandits can solve a huge range of real-world problems. However, current popular algorithms to solve them either rely on linear models or unreliable uncertainty estimation in non-linear models, which are required to deal with the exploration ...
Adam Elwood +3 more
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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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Automated Personalized Goal Setting for Individual Exercise Behavior: Protocol for a Web-Based Adaptive Intervention Trial [PDF]
JMIR Research ProtocolsBackgroundThe incidence of chronic diseases associated with physical inactivity is on the rise, being one of the leading risk-increasing factors for early death rates throughout the world.
Juan Carlos Caro +2 more
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Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis [PDF]
EntropyWe study stochastic linear contextual bandits (CB) where the agent observes a noisy version of the true context through a noise channel with unknown channel parameters. Our objective is to design an action policy that can “approximate” that of a Bayesian
Sharu Theresa Jose, Shana Moothedath
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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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A digital twin-based comparative reinforcement learning framework for personalized behavioral recommendation [PDF]
Frontiers in Artificial IntelligencePromoting healthy lifestyle behaviors such as physical activity, sleep, diet quality, stress management, hydration, and healthy habits requires adaptive systems capable of responding dynamically to changing behavioral and environmental conditions ...
Ayan Chatterjee, Nurilla Avazov
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Personalized Digital Care Program Allocation for Older Adults: Reinforcement Learning-Based Simulation Study [PDF]
JMIR AgingBackgroundAs the demand for innovative older adult care grows alongside a shortage of care workers, personalization is key to optimizing services and enhancing long-term sustainability.
Hongsoo Kim +3 more
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Designing digital health interventions with causal inference and multi-armed bandits: a review [PDF]
Frontiers in Digital HealthRecent statistics from the World Health Organization show that non-communicable diseases account for 74% of global fatalities, with lifestyle playing a pivotal role in their development. Promoting healthier behaviors and targeting modifiable risk factors
Radoslava Švihrová +7 more
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