This review systematically examines two types of algorithms: passive methods (e.g., sliding-window UCB, discounted UCB), which filter out outdated data’s interference through forgetting mechanisms; and active methods (e.g., TS-CD, R-BOCPD), which dynamically update their parameters by detecting changes.
. Multi-armed bandit (MAB) algorithms are commonly used in sequential decision tasks such as online recommendation and advertising. In real-world systems, algorithms often have limited time and data to learn from, and poor decisions can be costly. From a finite-horizon perspective, this paper presents a mechanism-level...
Yi-Chen Fan· Proceedings of the 4th Inter...· 0 citations
: Recommendation systems are used in various online services like streaming media services, e-commerce sites and news apps to enable users to find content of their choice quickly. This often involves sequential decision-making without knowing the rewards or pay-offs that the recommendations yield. A classic example of...
Zi-Jie Zhao· Proceedings of the 4th Inter...· 0 citations
Critic-free reinforcement fine-tuning (RFT) for agentic large language models is often done through GRPO-style methods, which compute a group baseline over repeated rollouts to reduce target variance. However, this setup is ill-suited to agents acting in stateful environments such as live services or security sandboxes...
Joery A. de Vries, Neil D. Lawrence, Zhen-Wen Dai· 0 citations
This work introduces Decision-Relevant Fresh Comparison (DRFC), which uses new, balanced samples from all agents to compare arms at the network level and switches only when fresh global evidence indicates that the common best arm has changed, and proves a high-probability dynamic regret bound with no adaptation term de...
The results provide a modular framework that can be used to analyze linear bandit algorithms and explicitly connect quality of parameter estimation to optimal regret accumulation.
Arda Güçlü, Subhonmesh Bose, J. Birge· 0 citations
In practical federated learning (FL) environments, clients often possess non-IID data, which can degrade model performance and extend convergence times. Effective client selection strategies have emerged as a promising approach to mitigate the challenges posed by statistical heterogeneity across clients. This paper pro...
Jennifer Allsop, Nathan Gaw· IISE Annual Conference &...· 0 citations
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