Results 71 to 80 of about 497,803 (209)
On the Optimal Regret of Locally Private Linear Contextual Bandit [PDF]
Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research. Recently, there has been increasing interest in designing \emph{locally private} linear contextual bandit ...
Li, Jiachun +2 more
core +1 more source
Enhancing Dynamic Movie Recommendations With User Expectation Ratings in Contextual Bandit Models [PDF]
ITM Web of ConferencesModern movie recommendation systems face challenges such as dynamic personalization and real-time adaptability. Traditional methods like collaborative filtering and content-based recommendations struggle with dynamic user preferences and cold-start ...
Sun Weiye
doaj +1 more source
Australian Journal of Politics &History, EarlyView.ABSTRACT This study traces how gambling in Australia changed from a simple form of leisure, enjoyed by all social classes in colonial times, to a billion‐dollar industry driven by political and economic imperatives. Using a case study, it links vertical fiscal imbalance (VFI), caused by changes to the state‐federal taxation structure, to the ...
Barbara Kinder, Michael B. Charles
wiley +1 more source
Batched Nonparametric Contextual Bandits
IEEE Transactions on Information TheoryWe study nonparametric contextual bandits under batch constraints, where the expected reward for each action is modeled as a smooth function of covariates, and the policy updates are made at the end of each batch of observations. We establish a minimax regret lower bound for this setting and propose a novel batch learning algorithm that achieves the ...
Rong Jiang, Cong Ma 0001
openaire +5 more sources
Contextual Bandits with Side-Observations
CoRR, 2020 under ...
Rahul Singh 0001 +3 more
openaire +2 more sources
Distributionally Robust Batch Contextual Bandits
Management Science, 2023 Policy learning using historical observational data are an important problem that has widespread applications. Examples include selecting offers, prices, or advertisements for consumers; choosing bids in contextual first-price auctions; and selecting medication based on patients’ characteristics.
Nian Si +3 more
openaire +4 more sources
A Contextual Bandit-Based Adaptive OFDM-IM System Against Eavesdroppers
IEEE Open Journal of the Communications SocietyIn this paper, we propose an adaptive orthogonal frequency division multiplexing with index modulation (OFDM-IM) communication framework for reliable encrypted transmission in the presence of a passive eavesdropper.
Ufuk Altun, Ertugrul Basar
doaj +1 more source
Optimizing Ad Recommendations Using A Bayesian Multi-Armed Bandit Approach [PDF]
ITM Web of ConferencesThe contextual multi-armed bandit problem in online advertising recommendation represents a core challenge in recommender systems. Traditional approaches, including Linear Upper Confidence Bound (LinUCB) and Feature-based Collaborative Filtering ...
Zeng Yijian
doaj +1 more source
British Journal of Educational Technology, EarlyView.Abstract Cognitive presence and social presence are essential for computer‐supported collaborative learning (CSCL). Yet, monitoring and providing personalised feedback to support these presences remain a persistent challenge, particularly in the context of social annotation (SA).
Mengru Pan +3 more
wiley +1 more source
Contextual Multi-Armed Bandit With Costly Feature Observation in Non-Stationary Environments
IEEE Open Journal of Signal ProcessingMaximizing long-term rewards is the primary goal in sequential decision-making problems. The majority of existing methods assume that side information is freely available, enabling the learning agent to observe all features' states before making a
Saeed Ghoorchian +2 more
doaj +1 more source