Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models
Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncertainty propagation. This paper introduces an uncertainty-aware probabilistic latent transport framework that formulates domain adaptation as a stochastic geometric alignment problem in representation space. A Bayesian transport operator is proposed to redistribute latent probability mass along Wasserstein-type geodesic trajectories, while a PAC-Bayesian regularization mechanism constrains posterior model complexity to mitigate catastrophic overfitting. The proposed formulation yields theor...
| Authors | Aueaphum Aueawatthanaphisut, Kuepon Auewattanapisut |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 26 March 2026 |
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