arXiv:2606.25265v1 Announce Type: new Abstract: Particle-based variational inference (ParVI) methods approximate an intractable target distribution by evolving an ensemble of interacting samples. Existing approaches rely predominantly on kernel-based repulsion (e.g., SVGD), which suffers from variance collapse in high dimensions and mode collapse on multimodal targets -- pathologies caused by the absence of global transport structure. We introduce entropic transport descent (ETD), a ParVI family that frames each particle update as an entropy-regularized optimal transport problem. Derived from
This paper introduces a new method for variational inference, leveraging optimal transport concepts, which is a rapidly evolving area in machine learning to address limitations of existing techniques.
Improved variational inference methods lead to more robust and accurate AI models, particularly in high-dimensional or multimodal contexts, impacting multiple AI applications.
The proposed 'entropic transport descent' offers a potential pathway to overcome current limitations in particle-based variational inference, such as mode and variance collapse.
- · AI researchers
- · Machine learning developers
- · Sectors using complex AI models
- · Developers reliant solely on older kernel-based methods
This research provides a more stable and effective method for approximating complex probability distributions within AI models.
Enhanced inference capabilities could lead to breakthroughs in areas requiring high-dimensional data analysis and uncertainty quantification.
More reliable and generalizable AI could accelerate scientific discovery and enterprise automation across various domains.
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Read at arXiv cs.LG