Results 111 to 120 of about 497,803 (209)

Balanced Linear Contextual Bandits

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning.
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens   +3 more
openaire   +4 more sources

Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting. [PDF]

J Am Stat Assoc, 2021
Chen H, Lu W, Song R.
europepmc   +1 more source

Tree Ensembles for Contextual Bandits

Trans. Mach. Learn. Res.
We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both standard and combinatorial settings. As part of this framework, we propose a novel method of estimating the uncertainty in tree ensemble predictions.
Hannes Nilsson, Rikard Johansson, Niklas Åkerblom, Morteza Haghir Chehreghani   +3 more
openaire   +3 more sources

Linear Contextual Bandits with Knapsacks

, 2015
We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm.
Shipra Agrawal 0001, Nikhil R. Devanur
openaire   +3 more sources

Action Centered Contextual Bandits

Advances in neural information processing systems, 2017
to appear at NIPS ...
Kristjan H. Greenewald, Ambuj Tewari, Susan A. Murphy, Predrag V. Klasnja   +3 more
openaire   +4 more sources

Strategic Linear Contextual Bandits

Advances in Neural Information Processing Systems 37
To appear at NeurIPS ...
Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu   +3 more
openaire   +4 more sources

Contextual Bandits with Stochastic Experts

CoRR, 2018
20 pages, 2 Figures, Accepted for publication in AISTATS ...
Sen, Rajat, Shanmugam, Karthikeyan, Sharma, Nihal, Shakkottai, Sanjay   +3 more
openaire   +3 more sources

[News Clip: Pants bandit]

, 1958
Video footage from the WBAP-TV station in Fort Worth, Texas to accompany a news story about Dallas Police apprehending the so-called pants ...
WBAP-TV (Television station : Fort Worth, Tex.)
core   +2 more sources

Time-Varying Preference Bandits for Robot Behavior Personalization

Applied Sciences
Robots are increasingly employed in diverse services, from room cleaning to coffee preparation, necessitating an accurate understanding of user preferences.
Chanwoo Kim, Joonhyeok Lee, Eunwoo Kim, Kyungjae Lee   +3 more
doaj   +1 more source

Combining Cmab with Matrix Factorization and Clustering For Enhanced Movie Recommendations [PDF]

ITM Web of Conferences
The contextual multi-armed bandit (CMAB) algorithm faces problems of a large-scale, sparse data matrix and many arms. This paper proposes a method that combines matrix factorization with a clustering algorithm to enhance the performance of the CMAB ...
Xiong Shuyue
doaj   +1 more source