The Practicality of Normalizing Flow Test-Time Training in Bayesian Inference for Agent-Based Models
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Abstract (arXiv)
Agent-Based Models (ABMs) are gaining great popularity in economics and social science because of their strong flexibility to describe the realistic and heterogeneous decisions and interaction rules between individual agents. In this work, we investigate for the first time the practicality of test-time training (TTT) of deep models such as normalizing flows, in the parameters posterior estimations of ABMs. We propose several practical TTT strategies for fine-tuning the normalizing flow against distribution shifts. Our numerical study demonstrates that TTT schemes are remarkably effective, enabling real-time adjustment of flow-based inference for ABM parameters.
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References · 5
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
- 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 50
- 2023Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Modelscited by 19
- 2023NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problemscited by 4
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