In-Context Learning for Full Bayesian Inference
Keywords: In-Context Learning, Prior-Fitted-Networks, Bayesian Inference
TL;DR: We demonstrate that in-context learning can be effectively used for full Bayesian inference on real-world data.
Abstract: Transformers have emerged as the dominant architecture in the field of deep learning, with a broad range of applications and remarkable in-context learning (ICL) capabilities. While not yet fully understood, ICL has already proved to be an intriguing phenomenon, allowing transformers to learn in-context---without requiring further training. In this paper, we further advance the understanding of ICL by demonstrating that transformers can perform full Bayesian inference for commonly used statistical models in-context. More specifically, we introduce a general framework that builds on ideas from prior fitted networks and continuous normalizing flows and enables us to infer complex posterior distributions for models such as generalized linear models and latent factor models. Extensive experiments on real-world datasets demonstrate that our ICL approach yields posterior samples that are similar in quality to state-of-the-art MCMC or variational inference methods that do not operate in-context. The source code for this paper is available at https://anonymous.4open.science/r/ICL_For_Full_Bayesian_Inference-3F53
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Submission Number: 232
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