Density ratio tricks

December 6, 2022 — December 6, 2022

approximation
Bayes
feature construction
likelihood free
machine learning
measure
metrics
probability
statistics
time series

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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, Louis C, Klein, Archambeau, et al. 2020. “Bayesian Optimization by Density Ratio Estimation.” In.
Tiao, Louis C., Klein, Seeger, et al. 2021. “BORE: Bayesian Optimization by Density-Ratio Estimation.” In Proceedings of the 38th International Conference on Machine Learning.
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.