mlsanityData to 14 Oct 2026

Etat de l'art sur l'application des bandits multi-bras

arXiv 2101.00001 ↗First posted 4 Jan 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

The Multi-armed bandit offer the advantage to learn and exploit the already learnt knowledge at the same time. This capability allows this approach to be applied in different domains, going from clinical trials where the goal is investigating the effects of different experimental treatments while minimizing patient losses, to adaptive routing where the goal is to minimize the delays in a network. This article provides a review of the recent results on applying bandit to real-life scenario and summarize the state of the art for each of these fields. Different techniques has been proposed to solve this problem setting, like epsilon-greedy, Upper confident bound (UCB) and Thompson Sampling (TS). We are showing here how this algorithms were adapted to solve the different problems of exploration exploitation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 44 of its 105 references that are in the map. First posted on arXiv 4 Jan 2021.

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44 of 105 references.

References · 44

  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
  2. 2019An ADMM Based Framework for AutoML Pipeline Configurationcited by 47
  3. 2019A Survey on Practical Applications of Multi-Armed and Contextual Banditscited by 45
  4. 2017Scalable Recollections for Continual Lifelong Learningcited by 33
  5. 2014A Neural Networks Committee for the Contextual Bandit Problemcited by 23
  6. 2018Beyond Backprop: Online Alternating Minimization with Auxiliary Variablescited by 23
  7. 2015Multi-armed Bandit Problem with Known Trendcited by 17
  8. 2018Low-Rank Bandit Methods for High-Dimensional Dynamic Pricingcited by 17
  9. 2018Incorporating Behavioral Constraints in Online AI Systemscited by 16
  10. 2017Context Attentive Bandits: Contextual Bandit with Restricted Contextcited by 14
  11. 2019Optimal Exploitation of Clustering and History Information in Multi-Armed Banditcited by 14
  12. 2019A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatrycited by 13
32 more
  1. 2020Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLcited by 11
  2. 2017Bandit Models of Human Behavior: Reward Processing in Mental Disorderscited by 8
  3. 2019How can AI Automate End-to-End Data Science?cited by 7
  4. 2013Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lenscited by 6
  5. 2017Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Modelscited by 6
  6. 2020Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behaviorcited by 6
  7. 2020Contextual Bandit with Missing Rewardscited by 6
  8. 2014Exponentiated Gradient Exploration for Active Learningcited by 4
  9. 2018Interpretable Multi-Objective Reinforcement Learning through Policy Orchestrationcited by 3
  10. 2019A Bandit Approach to Posterior Dialog Orchestration Under a Budgetcited by 3
  11. 2020Hyper-parameter Tuning for the Contextual Banditcited by 3
  12. 2020Online learning with Corrupted context: Corrupted Contextual Banditscited by 3
  13. 2013The Impact of Situation Clustering in Contextual-Bandit Algorithm for Context-Aware Recommender Systemscited by 2
  14. 2014Context-Based Information Retrieval in Risky Environmentcited by 2
  15. 2014Freshness-Aware Thompson Samplingcited by 2
  16. 2020Solving Constrained CASH Problems with ADMMcited by 2
  17. 2020Online Semi-Supervised Learning with Bandit Feedbackcited by 2
  18. 2013Role of temporal inference in the recognition of textual inferencecited by 1
  19. 2013Situation-Aware Approach to Improve Context-based Recommender Systemcited by 1
  20. 2013Optimizing an Utility Function for Exploration / Exploitation Trade-off in Context-Aware Recommender Systemcited by 1
  21. 2013Improving adaptation of ubiquitous recommander systems by using reinforcement learning and collaborative filteringcited by 1
  22. 2013Hybrid Q-Learning Applied to Ubiquitous recommender systemcited by 1
  23. 2013Towards User Profile Modelling in Recommender Systemcited by 1
  24. 2013Mobile Recommender Systems Methods: An Overviewcited by 1
  25. 2013Evolution of the user's content: An Overview of the state of the artcited by 1
  26. 2013Exponentiated Gradient LINUCB for Contextual Multi-Armed Banditscited by 1
  27. 2014Recommandation mobile, sensible au contexte de contenus 'evolutifs: Contextuel-E-Greedycited by 1
  28. 2014'Etude des dimensions sp'ecifiques du contexte dans un syst`eme de filtrage d'informationscited by 1
  29. 2014R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systemscited by 1
  30. 2018Contextual Bandit with Adaptive Feature Extractioncited by 1
  31. 2020Spectral Clustering using Eigenspectrum Shape Based Nystrom Samplingcited by 1
  32. 2020Computing the Dirichlet-Multinomial Log-Likelihood Functioncited by 1

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