Relevant bibliographies by topics / Offline Contextual Bandit

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Academic literature on the topic 'Offline Contextual Bandit'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Offline Contextual Bandit"

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Huang, Wen, and Xintao Wu. "Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20438–46. http://dx.doi.org/10.1609/aaai.v38i18.30027.

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This paper studies bandit problems where an agent has access to offline data that might be utilized to potentially improve the estimation of each arm’s reward distribution. A major obstacle in this setting is the existence of compound biases from the observational data. Ignoring these biases and blindly fitting a model with the biased data could even negatively affect the online learning phase. In this work, we formulate this problem from a causal perspective. First, we categorize the biases into confounding bias and selection bias based on the causal structure they imply. Next, we extract the
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Narita, Yusuke, Shota Yasui, and Kohei Yata. "Efficient Counterfactual Learning from Bandit Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4634–41. http://dx.doi.org/10.1609/aaai.v33i01.33014634.

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What is the most statistically efficient way to do off-policy optimization with batch data from bandit feedback? For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy. Our estimators are shown to have lowest variance in a wide class of estimators, achieving variance reduction relative to standard estimators. We then apply our estimators to improve advertisement design by a major advertisement company. Consistent with the theoretical result, our estimators allow us to improve on the existing bandit algorithm w
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Seifi, Farshad, and Seyed Taghi Akhavan Niaki. "Optimizing contextual bandit hyperparameters: A dynamic transfer learning-based framework." International Journal of Industrial Engineering Computations 15, no. 4 (2024): 951–64. http://dx.doi.org/10.5267/j.ijiec.2024.6.003.

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The stochastic contextual bandit problem, recognized for its effectiveness in navigating the classic exploration-exploitation dilemma through ongoing player-environment interactions, has found broad applications across various industries. This utility largely stems from the algorithms’ ability to accurately forecast reward functions and maintain an optimal balance between exploration and exploitation, contingent upon the precise selection and calibration of hyperparameters. However, the inherently dynamic and real-time nature of bandit environments significantly complicates hyperparameter tuni
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Krishnamurthy, Sanath Kumar, Tanmay Gangwani, Sumeet Katariya, Branislav Kveton, Shrey Modi, and Anshuka Rangi. "Selective Uncertainty Propagation in Offline RL." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17974–82. https://doi.org/10.1609/aaai.v39i17.33977.

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We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms. To learn this, it is sufficient to evaluate the treatment effect of deviating from the behavioral policy at step h after having optimized the policy for all future steps. Since the policy at any step can affect next-state distributions, the related distributional shift challenges can make this problem far more statistically hard than estimating such treatment effects in the stochastic contextual bandit settin
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Degroote, Hans, Patrick De Causmaecker, Bernd Bischl, and Lars Kotthoff. "A Regression-Based Methodology for Online Algorithm Selection." Proceedings of the International Symposium on Combinatorial Search 9, no. 1 (2021): 37–45. http://dx.doi.org/10.1609/socs.v9i1.18458.

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Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available when algorithms are selected and run. We investigate how this online data can be used to improve the selection model over time. This is especially relevant when insufficient training instances were used, but potentially improves the performance of algorithm selection in all cases. We
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Li, Zhao, Junshuai Song, Zehong Hu, Zhen Wang, and Jun Gao. "Constrained Dual-Level Bandit for Personalized Impression Regulation in Online Ranking Systems." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (2021): 1–23. http://dx.doi.org/10.1145/3461340.

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Impression regulation plays an important role in various online ranking systems, e.g. , e-commerce ranking systems always need to achieve local commercial demands on some pre-labeled target items like fresh item cultivation and fraudulent item counteracting while maximizing its global revenue. However, local impression regulation may cause “butterfly effects” on the global scale, e.g. , in e-commerce, the price preference fluctuation in initial conditions (overpriced or underpriced items) may create a significantly different outcome, thus affecting shopping experience and bringing economic los
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Bhatt, Umang, Valerie Chen, Katherine M. Collins, et al. "Learning Personalized Decision Support Policies." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14203–11. https://doi.org/10.1609/aaai.v39i13.33555.

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Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when will each form of support yield better outcomes? In this work, we posit that personalizing access to decision support tools can be an effective mechanism for instantiating the appropriate use of AI assistance. Specifically, we propose the general problem of learning a decision support policy that, for a given input, chooses which form of support to provide to decision-makers for whom we initially have no prior information. We develop Modiste, an interactive tool to learn personal
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Vera, Alberto, Siddhartha Banerjee, and Itai Gurvich. "Online Allocation and Pricing: Constant Regret via Bellman Inequalities." Operations Research 69, no. 3 (2021): 821–40. http://dx.doi.org/10.1287/opre.2020.2061.

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We develop a framework for designing simple and efficient policies for a family of online allocation and pricing problems that includes online packing, budget-constrained probing, dynamic pricing, and online contextual bandits with knapsacks. In each case, we evaluate the performance of our policies in terms of their regret (i.e., additive gap) relative to an offline controller that is endowed with more information than the online controller. Our framework is based on Bellman inequalities, which decompose the loss of an algorithm into two distinct sources of error: (1) arising from computation
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Aditya Kambhampati. "Advances in Personalized Investment Advisory through Reinforcement Learning: A Technical Review." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 187–93. https://doi.org/10.32996/jcsts.2025.7.4.22.

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Reinforcement learning (RL) represents a transformative technology in personalized investment advisory services, addressing fundamental limitations of traditional static approaches. This article explores the application of diverse RL frameworks to financial decision-making, from contextual multi-armed bandits for tactical allocations to full Markov Decision Processes for long-term planning. The integration of sophisticated state representations, multi-objective reward functions, and offline learning methodologies enables systems that adapt to individual investor behaviors while maintaining app
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Ayle, Morgane, Jimmy Tekli, Julia El-Zini, Boulos El-Asmar, and Mariette Awad. "BAR — A Reinforcement Learning Agent for Bounding-Box Automated Refinement." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2561–68. http://dx.doi.org/10.1609/aaai.v34i03.5639.

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Research has shown that deep neural networks are able to help and assist human workers throughout the industrial sector via different computer vision applications. However, such data-driven learning approaches require a very large number of labeled training images in order to generalize well and achieve high accuracies that meet industry standards. Gathering and labeling large amounts of images is both expensive and time consuming, specifically for industrial use-cases. In this work, we introduce BAR (Bounding-box Automated Refinement), a reinforcement learning agent that learns to correct ina
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Dissertations / Theses on the topic "Offline Contextual Bandit"

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Sakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.

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Cette thèse s'intéresse au problème de l'apprentissage à partir d'interactions en utilisant le cadre du bandit contextuel hors ligne. En particulier, nous nous intéressons à deux sujets connexes : (1) l'apprentissage de politiques hors ligne avec des certificats de performance, et (2) l'apprentissage rapide et efficace de politiques, pour le problème de recommandation à grande échelle. Pour (1), nous tirons d'abord parti des résultats du cadre d'optimisation distributionnellement robuste pour construire des bornes asymptotiques, sensibles à la variance, qui permettent l'évaluation des performa
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Conference papers on the topic "Offline Contextual Bandit"

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Huang, Yong, Charles A. Downs, and Amir M. Rahmani. "Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782277.

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Li, Lihong, Wei Chu, John Langford, and Xuanhui Wang. "Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms." In the fourth ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/1935826.1935878.

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Bouneffouf, Djallel, Srinivasan Parthasarathy, Horst Samulowitz, and Martin Wistuba. "Optimal Exploitation of Clustering and History Information in Multi-armed Bandit." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/279.

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We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms. The historical observations can contain any number of instances for each arm, and the pre-clustering information is a fixed clustering of arms provided as part of the input. We develop a variety of algorithms which incorporate this offline information effectively during the online exploration phase and derive their regret bounds. In particular, we develop the META algorithm which effectively hedges between two other algorithms: one which uses both histori
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Degroote, Hans. "Online Algorithm Selection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/746.

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Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available while an algorithm selection method is in use. In this extended abstract, the online algorithm selection problem is considered. In online algorithm selection, additional data can be processed, and the selection model can change over time. This abstract details the online algorithm se
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Januszewski, Piotr, Dominik Grzegorzek, and Paweł Czarnul. "Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits." In 16th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012311000003636.

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Ameko, Mawulolo K., Miranda L. Beltzer, Lihua Cai, Mehdi Boukhechba, Bethany A. Teachman, and Laura E. Barnes. "Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation." In RecSys '20: Fourteenth ACM Conference on Recommender Systems. ACM, 2020. http://dx.doi.org/10.1145/3383313.3412244.

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Yun, Joy, Allen Nie, Emma Brunskill, and Dorottya Demszky. "Exploring the Benefit of Customizing Feedback Interventions For Educators and Students With Offline Contextual Multi-Armed Bandits." In LAK '25: The 15th International Learning Analytics and Knowledge Conference. ACM, 2025. https://doi.org/10.1145/3706468.3706551.

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