Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
arXiv:2606.00309v1 Announce Type: new Abstract: Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm's hyperparameters in a principled manner to ensure the uncertainty estimates are statistically meaningful. In this work, we address this gap in tuning guidance by developing a statistical scaling limit theory for SGLD--Gibbs. We derive a joint asymptotic limit for the global parameters and latent variables under appropr
The proliferation of complex AI models necessitates more robust and reliable uncertainty quantification methods to ensure their responsible development and deployment.
Improved uncertainty quantification in latent variable models enhances the trustworthiness and interpretability of AI systems, crucial for high-stakes applications and reliable decision-making.
This research provides a principled approach to tune SGLD-Gibbs algorithms, leading to more statistically meaningful uncertainty estimates in large-scale latent variable models.
- · AI developers
- · Machine learning researchers
- · Industries relying on Bayesian inference
- · AI safety and ethics organizations
- · Developers of less rigorous uncertainty quantification methods
More accurate and reliable AI model evaluations, especially in complex generative and predictive tasks.
Accelerated adoption of advanced Bayesian inference techniques in production AI systems due to increased confidence in their outputs.
Potential for new AI applications requiring high statistical rigor and transparent uncertainty reporting, previously deemed too risky.
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Read at arXiv cs.LG