Active Learning for Stochastic Contextual Linear Bandits
A key goal in stochastic contextual linear bandits is to efficiently learn a near-optimal policy. Prior algorithms for this problem learn a policy by strategically sampling actions but naively (passively) sampling contexts from the underlying context distribution. However, in many practical scenarios -- including online content recommendation, survey research, and clinical trials -- practitioners can actively sample or recruit contexts based on prior knowledge of the context distribution. Despite this potential for active learning, the role of strategic context sampling in stochastic contextual linear bandits is underexplored. We propose an algorithm that learns a near-optimal policy by s...
| Authors | Emma Brunskill, Ishani Karmarkar, Zhaoqi Li |
|---|---|
| Primary category | cs.LG |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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