aiqbench analysis
This arXiv preprint is squarely about machine learning: it proposes a sample-efficient training method for deep implicit generative models using shrinkage priors, a generalized Bayes scoring-rule posterior, and block SGMCMC within Gibbs sampling. The reported evidence is limited to high-dimensional linear regression and a neural generative-model application, so readers should treat the sparsity gains as promising but not yet demonstrated at the scale of modern generative systems. The practical question to watch is whether this Bayesian sparsification can cut parameter counts or data requirements without sacrificing sample quality.
Shrinkage prior for implicit generative models
arXiv:2611.05241v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Computation (stat.CO)
arXiv:2611.05241 [cs.LG]
(or arXiv:2611.05241v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.05241 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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