https://github.com/acerbilab/amortized-conditioning-engine
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)
https://github.com/acerbilab/amortized-conditioning-engine
bayesian-optimization meta-learning neural-processes probabilistic-machine-learning simulation-based-inference
Last synced: about 1 month ago
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Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)
- Host: GitHub
- URL: https://github.com/acerbilab/amortized-conditioning-engine
- Owner: acerbilab
- License: apache-2.0
- Created: 2024-10-18T16:09:14.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-05T17:08:36.000Z (about 2 months ago)
- Last Synced: 2025-04-05T17:33:32.847Z (about 2 months ago)
- Topics: bayesian-optimization, meta-learning, neural-processes, probabilistic-machine-learning, simulation-based-inference
- Language: Jupyter Notebook
- Homepage: https://acerbilab.github.io/amortized-conditioning-engine/
- Size: 281 MB
- Stars: 13
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
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](https://arxiv.org/abs/2410.15320).
## Installation with Anaconda
To install the required dependencies, run:
```bash
conda install python=3.9.19 pytorch=2.2.0 torchvision=0.17.0 torchaudio=2.2.0 -c pytorch
pip install -e .
```
## Demos
At the moment, we release three demo notebooks with examples of our method, the Amortized Conditioning Engine (ACE).
- [`1.MNIST_demo.ipynb`](1.MNIST_demo.ipynb): Image completion demo with MNIST.
- [`2.BO_demo.ipynb`](2.BO_demo.ipynb): Bayesian optimization demo.
- [`3.SBI_demo.ipynb`](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.
## Citation
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}
}
```
## License
This code is released under the Apache 2.0 License.