arXiv:2606.11968v1 Announce Type: new Abstract: This paper studies efficient online algorithms for multinomial logistic bandits (MLogB), where the feedback distribution over $K+1$ outcomes follows a multinomial logistic model of $d$-dimensional action vectors. A representative UCB-type algorithm, OFUL-MLogB, achieves a regret bound of $\tilde{\mathcal{O}}(Kd\sqrt{T})$, but still requires $\mathcal{O}(K^3d^3)$ time and $\mathcal{O}(K^2d^2)$ space per round due to parameter estimation and optimistic reward construction, which is prohibitive in high-dimensional settings. To address this limitatio
This paper addresses computational efficiency for multinomial logistic bandits, a current bottleneck in large-scale AI applications due to growing data and model complexity.
Improved algorithms for contextual bandits will enable more efficient and scalable reinforcement learning systems, impacting sectors from advertising to robotics by making decision-making processes faster and less resource-intensive.
The development of more efficient algorithms for multinomial logistic bandits will reduce the computational cost and time required for certain types of online learning, making complex AI applications more feasible.
- · AI researchers
- · Companies deploying AI for real-time decision-making
- · Cloud computing providers (through increased efficiency demands)
- · Inefficient reinforcement learning models
- · Systems highly reliant on resource-intensive brute-force methods
Reduced computational overhead for certain online learning and recommendation systems.
Faster iteration and deployment of AI models in applications like personalized content delivery and autonomous systems.
Potentially democratizes access to advanced AI due to lower operational costs, broadening adoption.
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Read at arXiv cs.LG