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New KL Divergence Stability Bound Established for Gaussian Perturbations

By PulseAugur Editorial · [1 sources] ·

Researchers have established a new stability bound for the Kullback-Leibler (KL) divergence, particularly when its Gaussian reference distribution is perturbed. This advancement extends existing triangle inequalities, which previously required all three distributions to be Gaussian, thereby broadening their applicability in areas like out-of-distribution detection with flow-based generative models. The new bound, with a worst-case optimal $\sqrt{\epsilon}$ dependence, has been supported by experiments on synthetic distributions and latent representations from flow-based models. AI

IMPACT This theoretical advancement could improve the robustness and applicability of generative models in detecting out-of-distribution data.

RANK_REASON Academic paper detailing a new theoretical result in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New KL Divergence Stability Bound Established for Gaussian Perturbations

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Academic paper detailing a new theoretical result in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jialu Pan, Yufeng Zhang, Nan Hu, Zhenbang Chen, Ji Wang, Keqin Li ·

    Worst-Case Optimal $\sqrt{\epsilon}$-Rate Stability of KL Divergence under Perturbations of Gaussian Distributions

    arXiv:2604.11026v4 Announce Type: replace-cross Abstract: We establish a stability bound for the Kullback-Leibler (KL) divergence when its Gaussian reference distribution is perturbed. Existing relaxed triangle inequalities of KL divergence between Gaussian distributions require …

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