MMD DRO

class dro.linear_model.mmd_dro.MMD_DRO(input_dim, model_type='svm', fit_intercept=True, solver='MOSEK', sampling_method='bound')

Bases: BaseLinearDRO

MMD-DRO (Maximum Mean Discrepancy - Distributionally Robust Optimization) Implementation with flexible sampling methods and model types.

Reference: <https://arxiv.org/abs/2006.06981>

Initialize MMD-DRO with kernel-based ambiguity set.

Parameters:

  • input_dim (int) – Dimension of input features. Must match training data.

  • model_type (str) –

    Base model type. Supported:

    • 'svm': Support Vector Machine (hinge loss)

    • 'logistic': Logistic Regression (log loss)

    • 'ols': Ordinary Least Squares (L2 loss)

    • 'lad': Least Absolute Deviation (L1 loss)

  • sampling_method (str) –

    Supported:

    • 'bound'

    • 'hull'

  • fit_intercept (bool)

  • solver (str)

Raises:

ValueError –

  • If model_type not in supported list

  • If input_dim ≤ 0

  • If sampling_method is invalid

Example:
>>> model = MMD_DRO(input_dim=128, model_type='svm')
>>> model.sampling_method = 'hull' 
>>> model.eta = 0.5
update(config)

Update MMD-DRO model configuration.

Parameters:

config (Dict[str, Any]) –

Configuration dictionary containing optional keys:

  • eta (float):

    MMD radius controlling distributional robustness. Must satisfy \(\eta > 0\). Defaults to current value.

  • sampling_method (str):

    Ambiguity set sampling strategy. Valid options:

    • 'bound': Sample on MMD ball boundary

    • 'hull': Sample within convex hull

  • n_certify_ratio (float):

    Ratio of certification samples to training data size. Must satisfy \(0 < ext{ratio} \leq 1\). Defaults to current ratio.

Raises:

ValueError –

  • If eta is non-positive

  • If sampling_method not in {‘bound’, ‘hull’}

  • If n_certify_ratio ∉ (0, 1]

  • If config contains unrecognized keys

Return type:

None

Example:
>>> model = MMD_DRO(input_dim=10, model_type='svm')
>>> model.update({
...     'eta': 0.5,              
...     'sampling_method': 'hull' 
... })
fit(X, y, accelerate=True)

Vectorized implementation of MMD-DRO fit function.

Return type:

None

Parameters: