Adversarial Multi Class Labeling (AMCL)

Lightweight implementations of an adversarial learning scheme for multi class weakly supervised learning tasks.

Usage

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.

Citation

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{mazzetto2021adversarial,
  title={Adversarial multi class learning under weak supervision with performance guarantees},
  author={Mazzetto, Alessio and Cousins, Cyrus and Sam, Dylan and Bach, Stephen H and Upfal, Eli},
  booktitle={International Conference on Machine Learning},
  pages={7534--7543},
  year={2021},
  organization={PMLR}
}