Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems
arXiv:2605.30573v1 Announce Type: new Abstract: Sampling from high-dimensional, non-log-concave distributions with unnormalized densities remains a fundamental challenge in machine learning, particularly in black-box settings where gradient information is inaccessible or computationally prohibitive. While Langevin dynamics provides a principled framework for sampling when gradients are accessible, its extension to the black-box settings suffers from high variance and lacks non-asymptotic convergence guarantees for non-log-concave sampling. To address these limitations, we propose a variance-re
The continuous push for more efficient and robust machine learning models, especially in complex, black-box scenarios, necessitates innovations in foundational algorithms like sampling methods.
Improved sampling methods for non-log-concave distributions can unlock advancements in AI systems operating under uncertainty or with limited gradient information, expanding the applicability of machine learning.
This research introduces a method to perform efficient sampling in black-box AI environments, potentially leading to more reliable AI applications where gradient access is limited or computationally expensive previously.
- · AI researchers
- · Machine learning developers
- · Robotics
- · Scientific computing
- · Current inefficient black-box sampling methods
More robust and efficient AI models in challenging data environments.
Accelerated development of AI applications in domains with opaque or private data, leading to new commercial products.
Increased accessibility and utility of advanced AI, potentially democratizing sophisticated AI development beyond large-scale, gradient-rich datasets.
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Read at arXiv cs.LG