arXiv:2508.11931v3 Announce Type: replace Abstract: We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requiring a linear optimization oracle for the action sets in each round. Our approach reduces this setting to misspecification-robust adversarial linear bandits with fixed action sets. Without knowledge of the context distribution or access to a context simulator, the algorithm achieves $\widetilde{\mathcal{O}}(\min\{d^2\sqrt{T}, \sqrt{d^3T\log K}\})$ regret and runs in $\mathrm{poly}(d,T)$ time plus $
The continuous academic advancements in AI algorithms are a constant driving force, with researchers pushing performance boundaries in complex machine learning scenarios.
This development proposes a more efficient and robust algorithmic approach for contextual bandits, which are crucial for adaptive decision-making systems in dynamic environments.
The ability to achieve near-optimal regret with reduced computational overhead, especially without prior knowledge of context distribution, improves the practical viability of these systems.
- · AI developers
- · Reinforcement learning researchers
- · SaaS companies utilizing adaptive algorithms
- · Automated decision-making systems
- · Inefficient algorithms
- · Systems requiring extensive prior knowledge
- · Legacy adaptive decision systems
More robust and efficient AI agents can be developed for dynamic, real-world applications.
Increased adoption of autonomous AI in complex, uncertain environments due to improved performance guarantees.
Acceleration of automation in sectors requiring continuous learning and adaptation, potentially impacting white-collar workflows.
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Read at arXiv cs.LG