---
title: "Shrinkage prior for implicit generative models"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05241v1"
permalink: "https://feed.astralor.com/items/itm_489245df24ca2f7a42247ec6"
published: "2026-11-03T17:24:13.000Z"
score: 58
channel: "tech"
tags: ["推理效率","RAG","Shrinkage","Implicit","They","Our","Bayes","However"]
---

# Shrinkage prior for implicit generative models

## 速览

Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation. They successfully model complex random objects arising in real-world pro…

## 关键点

- Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation.
- They successfully model complex random objects arising in real-world problems.
- In this work, we propose a sample-efficient training mechanism for these models.

## 全文

Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation. They successfully model complex random objects arising in real-world problems. In this work, we propose a sample-efficient training mechanism for these models. Our approach leverages regularized inference with shrinkage priors and generalized Bayes. In the large $p$ (parameter dimension) and small $n$ (sample size) setting, sparsity-inducing priors have been successfully used for linear regression when $p \gg n$. However, their application to implicit generative models remains challenging due to the intractability of the likelihood function. To address this, we adapt a scoring rule posterior based on a strictly proper scoring rule, following the generalized Bayes framework. We then propose a block SGMCMC within Gibbs sampling mechanism. This enables efficient exploration of high-dimensional parameter spaces by identifying a sparse representation of neural implicit generative models. We demonstrate the performance of our proposed method through experiments on $p \gg n$ linear regression and an application of neural generative models.

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