Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits
arXiv:2602.09456v2 Announce Type: replace Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward function approximation to offline regression. The framework allows near-optimal regret for contextual bandits with large action spaces with $O(\log T)$ calls to an offline regression oracle over $T$ rounds, and makes $O(\log\log T)$ calls when $T$ is known. The design of OE2D algorithm generalizes Falcon~\citep{simchi2022bypassing} and its linear reward version~\citep[][Section 4]{xu2020upper} in
The paper provides a significant advancement in the efficiency and practicality of contextual bandit algorithms, critical for real-world AI applications evolving rapidly.
Advanced contextual bandit frameworks enhance AI's ability to make real-time decisions, impacting areas from recommendation systems to autonomous agents with improved sample efficiency.
This research introduces a unified framework allowing near-optimal regret with fewer calls to offline regression oracles, making contextual bandit learning more scalable and efficient across diverse applications and large action spaces.
- · AI platform developers
- · E-commerce & Advertising
- · Robotics researchers
- · Reinforcement learning practitioners
- · Inefficient sequential decision-making systems
- · Companies reliant on brute-force exploration
Improved contextual bandit algorithms will lead to more intelligent and adaptive AI systems in production.
Enhanced decision-making AI could accelerate automation in various industries, streamlining operations and reducing human intervention.
The widespread adoption of these efficient learning frameworks could further blur the lines between traditional software and adaptive AI agents, transforming business models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG