arXiv Machine Learning By Yujie Liu, Vincent Y. F. Tan, Yunbei Xu

Finite-Time Minimax Bounds and an Optimal Lyapunov Policy in Queueing Control

Read the original on arXiv Machine Learning →

arXiv:2506. 18278v4 Announce Type: replace-cross Abstract: We introduce an original minimax framework for finite-time performance analysis in queueing control and propose a surprisingly simple Lyapunov-based scheduling policy with superior finite-time performance.

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arXiv Machine Learning
Jul 3

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control

arXiv:2604. 08580v2 Announce Type: replace-cross Abstract: Reward fine-tuning of diffusion and flow models and sampling from tilted or Boltzmann distributions can both be formulated as stochastic optimal control (SOC) problems, where learning an optimal generative dynamics corresponds to optimizing a control under SDE constraints.

By Carles Domingo-Enrich, Jiequn Han
arXiv Machine Learning
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Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints

arXiv:2608. 01745v1 Announce Type: new Abstract: Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover control under hard finite-horizon energy and handover budgets, which induces a fundamental tension between BS-side energy management and user-side handover regulation.

By Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong, Songnam Hong