Thresholding Graph Bandits with GrAPL

Daniel LeJeune, Gautam Dasarathy, Richard Baraniuk
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2476-2485, 2020.

Abstract

In this paper, we introduce a new online decision making paradigm that we call Thresholding Graph Bandits. The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold. While traditionally in such problems, the arms are assumed to be independent, in our paradigm we further suppose that we have access to the similarity between the arms in the form of a graph, allowing us to gain information about the arm means with fewer samples. Such a feature is particularly relevant in modern decision making problems, where rapid decisions need to be made in spite of the large number of options available. We present GrAPL, a novel algorithm for the thresholding graph bandit problem. We demonstrate theoretically that this algorithm is effective in taking advantage of the graph structure when the structure is reflective of the distribution of the rewards. We confirm these theoretical findings via experiments on both synthetic and real data.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-lejeune20a, title = {Thresholding Graph Bandits with GrAPL}, author = {LeJeune, Daniel and Dasarathy, Gautam and Baraniuk, Richard}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {2476--2485}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/lejeune20a/lejeune20a.pdf}, url = { http://proceedings.mlr.press/v108/lejeune20a.html }, abstract = {In this paper, we introduce a new online decision making paradigm that we call Thresholding Graph Bandits. The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold. While traditionally in such problems, the arms are assumed to be independent, in our paradigm we further suppose that we have access to the similarity between the arms in the form of a graph, allowing us to gain information about the arm means with fewer samples. Such a feature is particularly relevant in modern decision making problems, where rapid decisions need to be made in spite of the large number of options available. We present GrAPL, a novel algorithm for the thresholding graph bandit problem. We demonstrate theoretically that this algorithm is effective in taking advantage of the graph structure when the structure is reflective of the distribution of the rewards. We confirm these theoretical findings via experiments on both synthetic and real data.} }
Endnote
%0 Conference Paper %T Thresholding Graph Bandits with GrAPL %A Daniel LeJeune %A Gautam Dasarathy %A Richard Baraniuk %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-lejeune20a %I PMLR %P 2476--2485 %U http://proceedings.mlr.press/v108/lejeune20a.html %V 108 %X In this paper, we introduce a new online decision making paradigm that we call Thresholding Graph Bandits. The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold. While traditionally in such problems, the arms are assumed to be independent, in our paradigm we further suppose that we have access to the similarity between the arms in the form of a graph, allowing us to gain information about the arm means with fewer samples. Such a feature is particularly relevant in modern decision making problems, where rapid decisions need to be made in spite of the large number of options available. We present GrAPL, a novel algorithm for the thresholding graph bandit problem. We demonstrate theoretically that this algorithm is effective in taking advantage of the graph structure when the structure is reflective of the distribution of the rewards. We confirm these theoretical findings via experiments on both synthetic and real data.
APA
LeJeune, D., Dasarathy, G. & Baraniuk, R.. (2020). Thresholding Graph Bandits with GrAPL. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:2476-2485 Available from http://proceedings.mlr.press/v108/lejeune20a.html .

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