Implement dual ridge regression for memory optimization

[KartikP]

Ridge and RidgeCV regression now use the dual kernel form when n_samples < n_features. This is common in Brain-Score which there are only hundreds of stimuli but thousands to hundreds of thousands of model features. This avoids materializing the coefficient matrix (n_features, n_targets) which can produce OOM for large models on fMRI benchmarks.

This PR adds DualRidgeRegression and DualRidgeCVRegression in the metrics/regression_correlation/metric.py.

Instead of solving for the full weight matrix, we precompute a small project matrix and apply it to the neural target in chunks.

This PR effectively reduces a coefficient matrix for ViT-L at 148k features on Allen2022 at 52k neuroids stored at 8 bytes from 62GB down to ~1MB.

Validation

• All 4 Allen2022 surface ridge benchmarks produce identical raw scores
• Identical predictions across alpha values
• RidgeCV selects the sample alpha as sklearn