This repository contains the implementation of the code for the paper "Stationary MMD Points for Cubature". See below for an illustration of stationary MMD points.

To install the required packages, run the following command:

pip install -r requirements.txt

To reproduce the results for our proposed stationary MMD points in the MoG experiment (Figure 3 (Top row)), run the following command:

python main.py --seed 0 --particle_num 100 --dataset mog --step_size 1.0 --bandwidth 1.0 --inject_noise_scale 0.0 --step_num 10000

You can vary the number of particles $n$ by altering the argument '--particle_num 100'. The baseline methods: Support Points (SP), Kernel Herding (KH), Kernel Thinning (KT), and Quasi-Monte Carlo (QMC) can also be run using the same configuration by replacing main.py with the corresponding Python script for each method. The number of particles $n$ for kernel thinning must be a power of 2.

To reproduce the results for the openML dataset (Figure 3 (Bottom row)), run:

python main.py --seed 0 --particle_num 100 --dataset elevators --step_size 0.3 --bandwidth 1.0 --inject_noise_scale 1.0 --step_num 100000

python main.py --seed 0 --particle_num 100 --dataset house_8L --step_size 1.0 --bandwidth 1.0 --inject_noise_scale 1.0 --step_num 100000

You can vary the number of particles $n$ by altering the argument '--particle_num 100'. The baseline methods: Support Points (SP), Kernel Herding (KH), and Kernel Thinning (KT) can also be run by replacing main.py with the corresponding Python script for each method. The number of particles $n$ for kernel thinning must be a power of 2.