arXiv:2606.31449v1 Announce Type: new Abstract: We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity. At each round, the learner is presented with $N$ sets of items, where each item is represented by a $d$-dimensional feature vector. The learner then constructs a slate by selecting one item per set; the resulting slate yields a scalar reward sampled from a Generalized Linear Model (GLM). We propose algorithms under two limited-adaptivity settings: (a) Batched and (b) Rarely-Switching. For the batched setting, we introduce B-SlateGLinCB, wh
This paper addresses a fundamental challenge in online recommendation and decision-making systems, aligning with the increasing sophistication and real-world deployment of AI models.
Sophisticated contextual bandit algorithms are crucial for improving the efficiency and adaptivity of AI systems in dynamic environments, with direct applications in advertising, content recommendation, and autonomous agents.
The proposed algorithms offer more robust and efficient methods for AI systems to learn and adapt under constraints, potentially leading to more scalable and predictable performance in complex real-world applications.
- · AI/ML researchers
- · Tech companies with recommendation engines
- · SaaS platforms employing AI agents
- · Systems relying on naive contextual bandit approaches
Improved performance and efficiency of AI-driven recommendation and decision systems.
Accelerated development and adoption of AI agents capable of operating effectively in dynamic environments with limited feedback.
Enhanced automation of complex tasks across various industries as AI systems become more adept at real-time adaptation and decision-making.
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Read at arXiv cs.LG