ICML Paper: Data-efficient Training of Machine Learning Models
- Change the flags in the code (line 22-23 mnist.py)
- Traing on random subsets: subset, random = True, True
- Traing on craig subsets: subset, random = True, False
- Traing on random subsets:
python train_resnet.py -s 0.1 -w -b 512
- Traing on craig subsets:
python train_resnet.py -s 0.1 -w -b 512 -g --smtk 0
- Traing on random subsets:
python logistic.py --data covtype --method sgd -s 0.1 --greedy 0
- Traing on craig subsets:
python logistic.py --data covtype --method sgd -s 0.1 --greedy 1
- You can use -b, -g to specify the learning rate, otherwise the learning rate will be tuned.
Please note that we used the greedy implementation from summary analythics, and the running times are reported accordingly. To use the provided python implementation, please use the flag smtk=0.