English(EN) Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems

新方法平滑组合优化策略

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-20 04:00

研究人员开发了一种新方法，通过添加受控的随机扰动来改进组合优化问题的策略训练。这种平滑技术使经验风险可微，有助于基于梯度的优化。该方法提供了一个泛化界限，将超额风险分解为扰动偏差、统计估计误差和优化误差，并引入了诸如扇形交叉概率和均匀有界密度等新概念来分析这些组成部分。 AI

影响为优化复杂决策问题引入了新颖的理论框架，有可能提高各种AI应用的效率。

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arXiv stat.ML TIER_1 English(EN) · Pierre-Cyril Aubin-Frankowski, Yohann De Castro, Axel Parmentier, Alessandro Rudi · 2026-05-20 04:00

组合优化问题的代理策略的泛化界限

arXiv:2407.17200v3 Announce Type: replace Abstract: Many real-world decision problems require solving, again and again, combinatorial optimization instances drawn from a common distribution. A recent line of structured learning methods exploits this regularity by learning policie…