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[2602.05139] Adaptive Exploration for Latent-State Bandits
Summary
The paper presents adaptive exploration strategies for latent-state bandits, addressing challenges in reward estimation and action selection in uncertain environments.
Why It Matters
This research is significant as it introduces innovative algorithms that improve decision-making in complex, dynamic settings where traditional methods struggle. By enhancing the understanding of latent states, it has implications for various applications in machine learning and AI.
Key Takeaways
- Introduces state-model-free bandit algorithms for better decision-making.
- Addresses issues of biased reward estimates in non-stationary environments.
- Empirical results show improved performance over classical approaches.
- Provides practical recommendations for algorithm selection in real-world scenarios.
- Combines computational efficiency with robust adaptation to changing rewards.
Computer Science > Machine Learning arXiv:2602.05139 (cs) [Submitted on 4 Feb 2026 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Adaptive Exploration for Latent-State Bandits Authors:Jikai Jin, Kenneth Hung, Sanath Kumar Krishnamurthy, Baoyi Shi, Congshan Zhang View a PDF of the paper titled Adaptive Exploration for Latent-State Bandits, by Jikai Jin and 4 other authors View PDF HTML (experimental) Abstract:The multi-armed bandit problem is a core framework for sequential decision-making under uncertainty, but classical algorithms often fail in environments with hidden, time-varying states that confound reward estimation and optimal action selection. We address key challenges arising from unobserved confounders, such as biased reward estimates and limited state information, by introducing a family of state-model-free bandit algorithms that leverage lagged contextual features and coordinated probing strategies. These implicitly track latent states and disambiguate state-dependent reward patterns. Our methods and their adaptive variants can learn optimal policies without explicit state modeling, combining computational efficiency with robust adaptation to non-stationary rewards. Empirical results across diverse settings demonstrate superior performance over classical approaches, and we provide practical recommendations for algorithm selection in real-world applications. Comments: Subjects: Machine Learning (cs.LG) MSC classes: 68T05 ACM classes: I.2.6 Cite as: arX...
