Personalization

1. Linear DRO Methods

2. NN-Based DRO Methods

2.1 Personalized Loss

For f-DRO and WDRO methods, our package supports personalized loss functions.

\(f\)-DRO

If the user would like to integrate his/her own loss functions, please

1. Create a new RobustLoss instance (from fdro.utils.py), and re-write the self._compute_individual_loss() function to user-specified forms.

2. Create a new Chi2NNDRO or CVaRNNDRO instance (from fdro_nn.py), and re-write the self._criterion() function with the newly-modified RobustLoss instance above.

WDRO

When personalizing the loss function for WDRO, please:

1. Create a new WNNDRO instance (from wdro_nn.py).

2. Re-write the self._loss() function.

2.2 Personalized Model Architecture

Users could pass their own model via self.update() function. Note that the personalized model must be written via PyTorch and is a sub-class of torch.nn.Module.

3. Tree-Based DRO Methods

For tree-based DRO methods, users could simply rewrite the self.loss() function