arXiv:2503.00565v3 Announce Type: replace-cross Abstract: The multi-armed bandits (MAB) framework is a widely used approach for sequential decision-making, where a decision-maker selects an arm in each round with the goal of maximizing long-term rewards. In many practical applications, such as personalized medicine and recommendation systems, contextual information is available at the time of decision-making, rewards from different arms are related rather than independent, and feedback is provided in batches. We propose a novel semi-parametric framework for batched bandits with covariates that
The continuous growth of machine learning applications, particularly in personalized systems and sequential decision-making, necessitates more sophisticated, efficient, and robust algorithmic approaches.
This research introduces a novel framework for batched multi-armed bandits with covariates, directly improving the efficiency and applicability of AI in real-world scenarios. It enhances decision-making in critical areas like personalized medicine and recommendation systems, leading to better outcomes and resource utilization.
This advancement proposes a new semi-parametric method that accounts for contextual information and related rewards in batched decision-making, moving beyond simpler models. This allows for more nuanced and effective sequential decision-making in complex systems.
- · AI/ML researchers
- · Healthcare providers (personalized medicine)
- · E-commerce platforms (recommendation systems)
- · AI infrastructure developers
- · Providers of less efficient MAB algorithms
- · Businesses relying on non-contextual decision models
Improved performance and efficiency of AI-driven personalized systems.
Accelerated adoption of advanced MAB techniques across various industries due to their enhanced practicality and accuracy.
Potentially, a shift in AI research focus towards semi-parametric and context-aware sequential decision models.
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Read at arXiv cs.LG