# Density ratio tricks

December 6, 2022 — December 6, 2022

approximation

Bayes

feature construction

likelihood free

machine learning

measure

metrics

probability

statistics

time series

Placeholder.

- talk-glouppe-cse21.pdf
- Masashi Sugiyama, Density Ratio Estimation in Machine Learning Slides
- Machine Learning Trick of the Day (7): Density Ratio Trick— Shakir Mohammed

## 1 References

Delaunoy, Hermans, Rozet, et al. 2022. “Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation.”

Fukumizu. 2003. “Likelihood Ratio of Unidentifiable Models and Multilayer Neural Networks.”

*The Annals of Statistics*.
Handcock, and Morris. 1998. “Relative Distribution Methods.”

*Sociological Methodology*.
Menon, and Ong. 2016. “Linking Losses for Density Ratio and Class-Probability Estimation.” In

*Proceedings of The 33rd International Conference on Machine Learning*.
Miller, Weniger, and Forré. 2022. “Contrastive Neural Ratio Estimation.” In.

Oliveira, Tiao, and Ramos. 2022. “Batch Bayesian Optimisation via Density-Ratio Estimation with Guarantees.” In

*Advances in Neural Information Processing Systems*.
Rozet, and Louppe. 2021. “Arbitrary Marginal Neural Ratio Estimation for Simulation-Based Inference.”

Sugiyama, Suzuki, and Kanamori. 2012.

*Density Ratio Estimation in Machine Learning*.
———. n.d. “Density Ratio Estimation: A Comprehensive Review.”

Sugiyama, Takeuchi, Suzuki, et al. 2010. “Conditional Density Estimation via Least-Squares Density Ratio Estimation.” In

*International Conference on Artificial Intelligence and Statistics*.
Thomas, Dutta, Corander, et al. 2020. “Likelihood-Free Inference by Ratio Estimation.”

Tiao, Klein, Archambeau, et al. 2020. “Bayesian Optimization by Density Ratio Estimation.” In.

Usman, Sud, Dufour, et al. 2020. “Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment.” In

*Advances in Neural Information Processing Systems*.