Results 21 to 30 of about 497,803 (209)
CoRR, 2023 Contextual bandit algorithms are at the core of many applications, including recommender systems, clinical trials, and optimal portfolio selection. One of the most popular problems studied in the contextual bandit literature is to maximize the sum of the rewards in each round by ensuring a sublinear regret against the best-fixed context-dependent ...
Siddhant Chaudhary, Abhishek Sinha
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Treatment effect optimisation in dynamic environments
Journal of Causal Inference, 2022 Applying causal methods to fields such as healthcare, marketing, and economics receives increasing interest. In particular, optimising the individual-treatment-effect – often referred to as uplift modelling – has peaked in areas such as precision ...
Berrevoets Jeroen +2 more
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Contextual Bandit-Based Amplifier IBO Optimization in Massive MIMO Network
IEEE Access, 2023 Massive Multiple-Input Multiple-Output (MMIMO) is one of the 5G key enablers. Though, most of the works consider MMIMO under assumptions of ideal hardware. It has been shown that Power Amplifiers (PAs) introduce nonlinear distortion while operating close
Marcin Hoffmann, Pawel Kryszkiewicz
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CoRR, 2015 We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context.
Miroslav Dudík +4 more
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Regularized Contextual Bandits
CoRR, 2018 AISTATS 2019, 23 pages, 2 ...
Xavier Fontaine +2 more
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Robust Contextual Linear Bandits
CoRR, 2022 Model misspecification is a major consideration in applications of statistical methods and machine learning. However, it is often neglected in contextual bandits. This paper studies a common form of misspecification, an inter-arm heterogeneity that is not captured by context.
Rong Zhu, Branislav Kveton
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Resourceful Contextual Bandits
CoRR, 2014 This is the full version of a paper in COLT 2014. Version history: (v2) Added some details to one of the proofs, (v3) a big revision following comments from COLT reviewers (but no new results), (v4) edits in related work, minor edits elsewhere. (v6) A correction for Theorem 3, corollary for contextual dynamic pricing with discretization; updated follow-
Ashwinkumar Badanidiyuru +2 more
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CoRR, 2019 Contextual Bandits is one of the widely popular techniques used in applications such as personalization, recommendation systems, mobile health, causal marketing etc . As a dynamic approach, it can be more efficient than standard A/B testing in minimizing regret.
Praneet Dutta +3 more
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Adapting to Misspecification in Contextual Bandits
CoRR, 2021 Appeared at NeurIPS ...
Dylan J. Foster +3 more
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Optimizing Neural Spike Train Prediction Using Contextual Bandit Algorithms Within Dqn Frameworks [PDF]
ITM Web of ConferencesThis study addresses key challenges in applying Deep Q-Networks (DQN) to neural spike train prediction by introducing a novel Contextual Bandit-enhanced DQN (CB-DQN) approach.
Feng Xingzhe
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