This code runs most of the experiments detailed in the paper.

The following commands run experiments with the same hyperparameters that were used in the paper (varying the noise rate):

nll:

• python3 main.py --dataset mnist --noise_rate 0.2 --lr 0.001 --batch_size 128 --noise_type symmetric --n_epoch 50 --lambda_type nll Gamblers + Early Stopping:
• python3 main.py --dataset mnist --noise_rate 0.2 --lr 0.001 --batch_size 128 --noise_type symmetric --n_epoch 50 --lambda_type gmblers --eps 9.9 --early_stopping Gamblers + Autoscheduling:
• python3 main.py --dataset mnist --noise_rate 0.2 --lr 0.001 --batch_size 128 --noise_type symmetric --n_epoch 50 --lambda_type euc

nll:

• python3 main.py --dataset mnist --noise_rate 0.2 --lr 0.001 --batch_size 128 --noise_type symmetric --n_epoch 50 --lambda_type nll Gamblers + Early Stopping:
• python3 main.py --dataset cifar10 --noise_rate 0.2 --lr 0.001 --batch_size 128 --noise_type symmetric --n_epoch 100 --start_gamblers 10 --lambda_type gmblers --eps 9.9 --early_stopping Gamblers + Autoscheduling:
• python3 main.py --dataset cifar10 --noise_rate 0.2 --lr 0.001 --batch_size 128 --noise_type symmetric --n_epoch 100 --start_gamblers 10 --lambda_type euc

nll:

• python3 main.py --dataset imdb --noise_rate 0.2 --lr 0.001 --batch_size 32 --noise_type symmetric --n_epoch 100 --start_gamblers 10 --lambda_type nll Gamblers + Early Stopping:
• python3 main.py --dataset imdb --noise_rate 0.2 --lr 0.001 --batch_size 32 --noise_type symmetric --n_epoch 100 --start_gamblers 10 --lambda_type gmblers --eps 1.95 --early_stopping Gamblers + Autoscheduling:
• python3 main.py --dataset imdb --noise_rate 0.2 --lr 0.001 --batch_size 32 --noise_type symmetric --n_epoch 100 --start_gamblers 10 --lambda_type euc

By manipulating the options, the full range of experiments involving Gambler's Loss in the paper are runnable.