Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
arXiv:2606.00293v1 Announce Type: new Abstract: Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty quantification remains challenging, particularly in the practically relevant settings when the batch size is large or the model is misspecified. Existing theory that provides tuning guidance relies on continuous-time limits or strong statistical assumptions, which can become quantitatively inaccurate in these regimes. We address these shortcomings by proposing new discrete-time approximations to SG(
The continuous evolution of AI algorithms and the increasing scale of models necessitate more robust and accurate uncertainty quantification methods.
Improved uncertainty quantification for AI models, especially in large-sample and misspecified settings, is critical for building more reliable and trustworthy AI systems across various applications.
This research provides a more accurate theoretical foundation and practical methods for tuning stochastic gradient-based algorithms, moving beyond limitations of existing approaches.
- · AI researchers
- · ML engineers
- · SaaS companies using AI
- · Sectors reliant on AI predictions
- · AI systems with poor uncertainty estimates
- · Methods relying on strong statistical assumptions
More reliable deployment of AI models in sensitive applications due to better understanding of their confidence.
Reduced risk and increased adoption of AI in fields like finance, healthcare, and autonomous systems.
Acceleration of research into safer and more interpretable AI, fostering greater public trust.
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Read at arXiv cs.LG