arXiv:2604.20024v2 Announce Type: replace Abstract: We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration. A bandit algorithm is $\rho$-replicable if two executions using shared internal randomness but independent reward realizations produce the same action sequence with probability at least $1-\rho$. Prior approaches to this problem are elimination-based and, in linear bandits with infinitely many actions, rely on discretization, leading to suboptimal dependence on the dimension $d$ and $\rho$. We develop
The paper addresses a critical challenge in machine learning research regarding the replicability and reliability of algorithms, particularly pertinent as AI systems become more complex and deployed in critical applications.
Improving the replicability of bandit algorithms enhances the trustworthiness and verifiable performance of AI systems, which is crucial for their adoption in high-stakes environments and scientific validation.
This research introduces a new approach to achieving replicability in bandit algorithms without the drawbacks of previous methods, potentially leading to more robust and reliable AI-driven decision-making.
- · AI researchers
- · Developers of AI agents
- · Sectors requiring high reliability in AI
- · Researchers or developers relying on non-replicable AI systems
The development of more reliable and auditable AI algorithms for decision-making processes.
Increased trust in AI systems leading to broader and more critical applications in various industries.
The establishment of new industry standards and regulatory frameworks emphasizing replicability and robustness in AI design.
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