RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits
Real-world contextual bandit problems with complex reward models are often tackled with iteratively trained models, such as boosting trees. However, it is difficult to directly apply simple and effective exploration strategies--such as Thompson Sampling or UCB--on top of those black-box estimators. Existing approaches rely on sophisticated assumptions or intractable procedures that are hard to verify and implement in practice. In this work, we explore the use of an exploration-free (pure-greedy) action selection strategy, that exploits the randomness inherent in model fitting process as an intrinsic source of exploration. More specifically, we note that the stochasticity in cross-validati...
| Authors | Tong Li, Thiago de Queiroz Casanova, Eric M. Schwartz et al. |
|---|---|
| Primary category | stat.ML |
| Published | 11 March 2026 |
| Last updated at source | 17 May 2026 |
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