Meta-probabilistic Modeling

• URL: http://arxiv.org/abs/2601.04462v1
• Date: Thu, 08 Jan 2026 00:34:06 GMT
• Title: Meta-probabilistic Modeling
• Authors: Kevin Zhang, Yixin Wang,
• Abstract summary: We propose meta-probabilistic modeling (MPM), a meta-learning algorithm that learns generative model structure directly from multiple related datasets.<n>For learning and inference, we propose a tractable VAE-inspired surrogate objective, and optimize it through bi-level optimization.<n>We evaluate MPM on object-centric image modeling and sequential text modeling, demonstrating that it adapts generative models to data while recovering meaningful latent representations.
• Score: 36.339664748241944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While probabilistic graphical models can discover latent structure in data, their effectiveness hinges on choosing well-specified models. Identifying such models is challenging in practice, often requiring iterative checking and revision through trial and error. To this end, we propose meta-probabilistic modeling (MPM), a meta-learning algorithm that learns generative model structure directly from multiple related datasets. MPM uses a hierarchical architecture where global model specifications are shared across datasets while local parameters remain dataset-specific. For learning and inference, we propose a tractable VAE-inspired surrogate objective, and optimize it through bi-level optimization: local variables are updated analytically via coordinate ascent, while global parameters are trained with gradient-based methods. We evaluate MPM on object-centric image modeling and sequential text modeling, demonstrating that it adapts generative models to data while recovering meaningful latent representations.

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