Lightweight implementations of an adversarial learning scheme for multi class weakly supervised learning tasks.
To use our methods, you first need to import the subgradient_method file with
import algorithms.subgradient_method as SG
Next, you should generate the constraints for the linear program. You can do this by calling
c_matrix, c_vector, c_sign = SG.compute_constraints_with_loss(loss_function, unlabeled_data, labeled_data, labels)
Then, you can run the linear program as follows:
initial_theta = np.random.normal(0, 0.1, (np.shape(test_data)[1], C))
model_theta = SG.subGradientMethod(unlabeled_data, c_matrix, c_vector,
c_sign, loss_function, model,
projection_function, initial_params,
T, h, N, num_unlab, C)
Here, this allows for different parameterized models by specifying the model and initial_params values. You can also change the objective funtion of the linear program by changing the particular loss_function parameter. Finally, there are a few other parameters that specify the training procedure, which you can customize (T, projection_function, etc.).
We have implementations of a few different models, loss functions, and projection functions, which can be used in the util.py file. You can also see an example usage of our methods on a toy dataset in the main.py file.
Please cite the following paper if you use our work. Thanks!
Alessio Mazzetto*, Cyrus Cousins*, Dylan Sam, Stephen H. Bach, and Eli Upfal. "Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees". International Conference on Machine Learning (ICML), 2021.
@inproceedings{amcl,
Author = {Mazzetto, A. and Cousins, C. and Sam, D. and Bach, S. H. and Upfal, E.},
Title = {Adversarial Multiclass Learning under Weak Supervision with Performance Guarantees},
Booktitle = {International Conference on Machine Learning (ICML)},
Year = {2021}}