研究人员开发了用于优化机器学习中决策过程的新理论框架。一篇论文介绍了基于后悔的贝叶斯优化停止准则，确保解以高概率在指定的ε-最优性范围内。另一项研究侧重于多项逻辑MDP的强化学习，提出了一种具有改进的最小极大最优后悔界限的算法。第三篇论文解决了折扣MDP中的风险敏感强化学习问题，在递归熵风险度量下提供了学习最优策略的样本复杂度界限。 AI
影响 这些理论进展可能带来更高效、更鲁棒的AI系统，以应对复杂的决策场景。
排序理由 该集群包含多篇详细阐述机器学习优化和强化学习理论进展的学术论文。
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arXiv:2605.22561v1 Announce Type: new Abstract: Bayesian optimization (BO) is a widely used iterative black-box optimization method that utilizes Gaussian process (GP) surrogate models. In practice, BO is typically terminated after a fixed evaluation budget is exhausted, which ca…
Bayesian optimization (BO) is a widely used iterative black-box optimization method that utilizes Gaussian process (GP) surrogate models. In practice, BO is typically terminated after a fixed evaluation budget is exhausted, which can incur unnecessary cost and provides no optimal…
arXiv:2605.19768v1 Announce Type: cross Abstract: We study reinforcement learning for episodic Markov Decision Processes (MDPs) whose transitions are modelled by a multinomial logistic (MNL) model. Existing algorithms for MNL mixture MDPs yield a regret of $\smash{\tilde{O}(dH^2\…
arXiv:2506.00286v3 Announce Type: replace-cross Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs with recursive entropic risk measures (ERM), where the risk parameter $\beta \neq 0$ controls the agent's risk attitude: $\beta>0$ for risk-averse an…
We study reinforcement learning for episodic Markov Decision Processes (MDPs) whose transitions are modelled by a multinomial logistic (MNL) model. Existing algorithms for MNL mixture MDPs yield a regret of $\smash{\tilde{O}(dH^2\sqrt{T})}$ (Li et al., 2024), where $d$ is the fea…