Code of the Median-of-Means Kernel Density Estimator (MoM-KDE) used in:

Robust Kernel Density Estimation with Median-of-Means principle.

The reposity contains codes for MoM-KDE, RKDE [1], and SPKDE [2].

This code runs on Python >= 3.5. Set up environment with:

pip install -r requirements.txt

Results rely on several runs over either real or synthetic data. For this purpose, there are three scripts to use.

Runs MoM-KDE and other robust KDEs over real data.
Scores are saved as a CSV in outputs/scores/.
One can select the datasets to be tested and several other parameters (see 'Parameters' section in script).

Runs MoM-KDE and other robust KDEs over synthetic data.
Scores are saved as a CSV in outputs/scores/. One can select the synthetic schemes to be tested and several other parameters (see 'Parameters' section in script).

Display results of selected dataset and metric (see 'Parameters' section in script)

For example, running successively compare_kde_methods.py and plot_results.py yields the following plots:

Likewise, running compare_kde_methods_synth.py and plot_results.py (changing the 'Parameters' section so that the synthetic dataset is selected and the Jensen divergence is displayed) yields:

[1] J. Kim and C. D. Scott. Robust kernel density estimation. Journal of Machine Learning Research, 13(Sep):2529–2565, 2012. [link]

[2] R. A. Vandermeulen and C. Scott. Robust kernel density estimation by scaling and projection in hilbert space. In Advances in Neural Information Processing Systems, pages 433–441, 2014. [link]