Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
arXiv:2606.09802v1 Announce Type: new Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time. Under practitioner-friendly assumptions, we reduce this setting to linear bandit with stationary mean but heteroskedastic and non-stationary noise. We further study the case when the learner must ensure the mean reward of each decision must exceed that of a baseline strategy $\bolds
The continuous drive for more efficient and adaptable AI systems, particularly in reinforcement learning, is leading to new research into handling real-world complexities like drifting contexts and personalized preferences.
Improving the efficiency and robustness of bandit algorithms can significantly enhance the performance of recommendation systems, A/B testing, and personalized content delivery across various industries.
This research provides a framework for developing more stable and context-aware experimentation and personalization systems, offering practical solutions for challenges common in dynamic online environments.
- · E-commerce platforms
- · Digital advertisers
- · Content recommendation services
- · AI/ML researchers
- · Inefficient A/B testing methodologies
- · Static recommendation systems
More accurate and responsive personalized user experiences based on advanced bandit algorithms.
Increased user engagement and conversion rates for platforms employing these sophisticated experimentation methods.
Accelerated development of general-purpose AI agents capable of autonomous and adaptive decision-making in complex, dynamic environments.
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Read at arXiv cs.LG