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Pages
R Closure
Published:
About Me
About me
Posts
An introduction to Density Ratio Estimation and its applications with Missing Data
Published:
Density ratio estimation is a highly useful field of mathematics with many applications. This post describes my research undertaken alongside my supervisors Song Liu and Henry Reeve which aims to make density ratio estimation robust to missing data. This work was recently published in proceedings for AISTATS 2023.
Density Ratio Estimation
Definition
As the name suggests, density ratio estimation is simply the task of estimating the ratio between two probability densities. More precisely for two RVs (Random Variables) $Z^0, Z^1$ on some space $\mathcal{Z}$ with probability density functions (PDFs) $p_0, p_1$ respectively, the density ratio is the function $r^{*}:\mathcal{Z}\rightarrow\mathbb{R}$ defined by \(r^{*}(z):=\frac{p_0(z)}{p_1(z)}\)
R closures and their surprising behaviours
Published:
Functional programming allows you to both use and create functions inside other functions. Using function as arguments works exactly the same as specifying other arguments however when creating functions within function there are a few more quirks to be aware of.
publications
Density Ratio Estimation and Neyman Pearson Classification with Missing Data
Published in In the proceedings of Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023
This paper adapts Density Ratio Estimation techniques making them robust to missing not at random missing data before applying this to the field of Neyman Pearson classification.
Recommended citation: Josh Givens, Song Liu, Henry Reeve, "Density Ratio Estimation and Neyman Pearson Classification with Missing Data." In the proceedings of Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023. https://proceedings.mlr.press/v206/givens23a.html
Conditional Outcome Equivalence: A Quantile Alternative to CATE
Published in In the proceedings of Advances in Neural Information Processing Systems, 2024
This paper presents an alternative to the CATE called the conditional quantile comparator which rpovide a more complete characterisation of the treatment effect while maintaining the CATE’s nice estimation properties.
Recommended citation: Josh Givens, Henry Reeve, Song Liu, Katarzyna Reluga, "Conditional Outcome Equivalence: A Quantile Alternative to CATE." In the proceedings of Advances in Neural Information Processing Systems, 2024. https://arxiv.org/abs/2410.12454
teaching
University of Bath - Tutorial Teaching
Undergraduate course toturials, University of Bath, Department of Mathematics, 2019
Led Tutorials for the undergraduate mathematics modules Algebra 1A & 1B.