This repository contains the python code for our ICML 2020 paper Robust Outlier Arm Identification. Used packages include: numpy, math, multiprocessing, copy, functools, astropy.stats, time, datetime and matplotlib.
Use the following command to reproduce our experiment in Figure 1.(b).
python3 termination_count.py
Use the following command to reproduce our experiments in Figure 2 and Figure 3 (set contamination_level = 0 in OAI_comparison.py).
python3 OAI_comparison.py
Use the following command to reproduce our experiment in Figure 4.
python3 ROAI.py
Take the following steps to reproduce our experiment in Figure 5. First, get dataset wine.mat from this website. Next, preprocess the dataset based on the experiment description. Then, input the preprocessed means of normal and outlier arms into y_normal and y_outlier (in file real_data.py). Finally, run the following command.
python3 real_data.py