glouppe / talk-neural-lfi

Neural Likelihood-free Inference

Online version at https://glouppe.github.io/talk-neural-lfi/index.html?p=talk.md.

This talk is a summary of the following papers:

• Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., Cranmer, K. (2019). Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning. arXiv preprint arXiv 1909.02005.
• Hermans, J., Begy, V., & Louppe, G. (2019). Likelihood-free MCMC with Approximate Likelihood Ratios. arXiv preprint arXiv:1903.04057.
• Baydin, A. G., Shao, L., Bhimji, W., Heinrich, L., Meadows, L., Liu, J., ... & Ma, M. (2019). Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. arXiv preprint arXiv:1907.03382.
• Stoye, M., Brehmer, J., Louppe, G., Pavez, J., & Cranmer, K. (2018). Likelihood-free inference with an improved cross-entropy estimator. arXiv preprint arXiv:1808.00973.
• Baydin, A. G., Heinrich, L., Bhimji, W., Gram-Hansen, B., Louppe, G., Shao, L., ... & Wood, F. (2018). Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. arXiv preprint arXiv:1807.07706.
• Brehmer, J., Louppe, G., Pavez, J., & Cranmer, K. (2018). Mining gold from implicit models to improve likelihood-free inference. arXiv preprint arXiv:1805.12244.
• Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J. (2018). Constraining Effective Field Theories with Machine Learning. arXiv preprint arXiv:1805.00013.
• Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J. (2018). A Guide to Constraining Effective Field Theories with Machine Learning. arXiv preprint arXiv:1805.00020.
• Casado, M. L., Baydin, A. G., Rubio, D. M., Le, T. A., Wood, F., Heinrich, L., ... & Bhimji, W. (2017). Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. arXiv preprint arXiv:1712.07901.
• Louppe, G., Hermans, J., & Cranmer, K. (2017). Adversarial Variational Optimization of Non-Differentiable Simulators. arXiv preprint arXiv:1707.07113.
• Cranmer, K., Pavez, J., & Louppe, G. (2015). Approximating likelihood ratios with calibrated discriminative classifiers. arXiv preprint arXiv:1506.02169.