Tree-AMP: Compositional Inference with Tree Approximate Message Passing
This is an incremental contribution that packages existing methods into a modular tool for researchers working on high-dimensional inference problems.
The authors introduced Tree-AMP, a Python package for compositional inference in high-dimensional tree-structured models, providing a unifying framework for various approximate message passing algorithms across tasks like generalized linear models and matrix factorization, with automated inference and theoretical performance prediction.
We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms previously derived for a variety of machine learning tasks such as generalized linear models, inference in multi-layer networks, matrix factorization, and reconstruction using non-separable penalties. For some models, the asymptotic performance of the algorithm can be theoretically predicted by the state evolution, and the measurements entropy estimated by the free entropy formalism. The implementation is modular by design: each module, which implements a factor, can be composed at will with other modules to solve complex inference tasks. The user only needs to declare the factor graph of the model: the inference algorithm, state evolution and entropy estimation are fully automated.