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New framework enables differentiability in sample-based inference

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel mathematical framework called Distributional Sensitivity Analysis to enable differentiability in sample-based inference. This framework provides analytical formulae for estimating the sensitivity of random samples with respect to distributional parameters, crucial for inverse problems in fields like nuclear physics. The method can be applied to black-box or simulation-based samplers without requiring model fitting or knowledge of sampling algorithms, and it facilitates integration into deep learning and automatic differentiation frameworks. AI

IMPACT Enables integration of arbitrary sampling subroutines into deep learning and automatic differentiation frameworks.

RANK_REASON The cluster contains an academic paper detailing a new mathematical framework and algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New framework enables differentiability in sample-based inference

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The cluster contains an academic paper detailing a new mathematical framework and algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Pi-Yueh Chuang, Ahmed Attia, Emil Constantinescu ·

    Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference

    arXiv:2508.09347v2 Announce Type: replace Abstract: This work introduces a mathematical framework for estimating the space-parameter sensitivity of random samples in arbitrary dimensions. Such sensitivity effectively acts as gradients of random samples with respect to distributio…

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