Bandit Profit-Maximization for Targeted Marketing
By Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
Rating
1494
Battle Count: 19
Relevance
7/10
While focused on marketing, the profit maximization framework and online learning techniques could be adapted for trading strategy optimization
Implementation Complexity
6/10
Algorithms are well-defined but require understanding of advanced bandit theory and convex optimization
Reproducibility
4/5
Algorithms and theoretical results are well-described, but no code repository is provided
About this paper
Methodology: Adversarial Bandit Algorithms. Problem types: Online Learning, Optimization.
The interactive Everscope explorer (charts, battles, favorites) loads below.