This repository will provide the implementation and code used in the AISTATS 2025 article Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., 2025). The full paper can be found on arXiv at: https://arxiv.org/abs/2410.15320.
To install the required dependencies, run:
conda install python=3.9.19 pytorch=2.2.0 torchvision=0.17.0 torchaudio=2.2.0 -c pytorch
pip install -e .At the moment, we release three demo notebooks with examples of our method, the Amortized Conditioning Engine (ACE).
1.MNIST_demo.ipynb: Image completion demo with MNIST.2.BO_demo.ipynb: Bayesian optimization demo.3.SBI_demo.ipynb: Simulation-based inference demo.
Each notebook demonstrates a specific application of ACE. Simply open the notebooks in Jupyter or in GitHub to visualize the demos.
Full code for this project will be made available later.
If you find this work valuable for your research, please consider citing our paper:
@article{chang2025amortized,
title={Amortized Probabilistic Conditioning for Optimization, Simulation and Inference},
author={Chang, Paul E and Loka, Nasrulloh and Huang, Daolang and Remes, Ulpu and Kaski, Samuel and Acerbi, Luigi},
journal={28th Int. Conf. on Artificial Intelligence & Statistics (AISTATS 2025)},
year={2025}
}
This code is released under the Apache 2.0 License.