https://github.com/acerbilab/amortized-conditioning-engine

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., 2024)
https://github.com/acerbilab/amortized-conditioning-engine

bayesian-optimization meta-learning neural-processes probabilistic-machine-learning simulation-based-inference

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Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., 2024)

• Host: GitHub
• URL: https://github.com/acerbilab/amortized-conditioning-engine
• Owner: acerbilab
• License: apache-2.0
• Created: 2024-10-18T16:09:14.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-22T06:03:02.000Z (2 months ago)
• Last Synced: 2024-10-23T11:02:06.407Z (2 months ago)
• Topics: bayesian-optimization, meta-learning, neural-processes, probabilistic-machine-learning, simulation-based-inference
• Language: Jupyter Notebook
• Homepage: https://arxiv.org/abs/2410.15320
• Size: 954 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• 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 preprint article *Amortized Probabilistic Conditioning for Optimization, Simulation and Inference* (Chang et al., 2024).

The full paper can be found on arXiv at: [https://arxiv.org/abs/2410.15320](https://arxiv.org/abs/2410.15320).

## 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.

Code to run the demos will be added soon, and full code for this project will be made available later.

### License

This code is released under the Apache 2.0 License.