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New CGMMD framework enables one-shot conditional sampling

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new framework called Conditional Generator using MMD (CGMMD) for generating samples from conditional distributions that are not fully observed. This method frames the training objective as a direct minimization problem without adversaries and allows for one-shot sampling in a single generator pass, reducing test-time complexity. The framework is demonstrated to perform competitively on synthetic tasks and practical applications like image denoising and super-resolution. AI

IMPACT Introduces a novel method for conditional sampling, potentially improving performance in areas like image processing and simulation-based inference.

RANK_REASON The cluster contains an academic paper detailing a new method for conditional sampling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New CGMMD framework enables one-shot conditional sampling

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The cluster contains an academic paper detailing a new method for conditional sampling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anirban Chatterjee, Sayantan Choudhury, Rohan Hore ·

    One-shot Conditional Sampling: MMD meets Nearest Neighbors

    arXiv:2509.25507v2 Announce Type: replace-cross Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image p…

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