English(EN) Generalization Bounds for Markov Algorithms through Entropy Flow Computations

新方法计算马尔可夫算法的泛化界

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种利用熵流计算来计算马尔可夫算法泛化界的新方法。该技术将先前仅限于特定噪声和算法结构（如 Langevin 动力学）的工作扩展到更广泛的由时间齐次马尔可夫过程控制的迭代动力学。新方法与修正的对数 Sobolev 不等式建立了联系，将泛化误差与马尔可夫过程的遍历性质联系起来，并为各种算法提供了新的界限。 AI

影响这项理论进展可能通过提供更严格的泛化界来促使开发更鲁棒、更可预测的机器学习模型。

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arXiv stat.ML TIER_1 English(EN) · Benjamin Dupuis, Maxime Haddouche, George Deligiannidis, Umut Simsekli · 2026-09-02 04:00

通过熵流计算的马尔可夫算法的泛化界限

arXiv:2502.07584v3 Announce Type: replace Abstract: Many learning algorithms can be represented as Markov processes, and understanding their generalization error is a central topic in learning theory. For specific continuous-time noisy algorithms, a prominent analysis technique r…

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通过熵流计算的马尔可夫算法的泛化界限

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