Selector Guide

Selectors choose which data points to include in the coreset for efficient unlearning. Instead of unlearning on the entire forget set, a well-chosen coreset can achieve similar results in less time.

When to Use Selectors

• Large forget sets — Reduce compute cost with a representative coreset

• Streaming deletions — Prioritise the most impactful data points

• Resource constraints — Run unlearning within a time/memory budget

Selector Categories

Influence-Based — Select by influence on model parameters:

• influence_functions — Compute Hessian-based influence scores

• tracin — Trace gradient inner products across checkpoints

Geometry-Based — Select by feature space coverage:

• herding — Greedy selection closest to class centroids

• k_center — Minimise maximum distance to nearest selected point

• facility_location — Maximise coverage of feature space

• craig — Coreset for Accelerating Incremental Gradient

Learning-Based — Select by training dynamics:

• forgetting_score — Points frequently forgotten during training

• active_learning — Uncertainty sampling for informative points

Gradient-Based — Select by gradient properties:

• gradient_matching — Match full-set gradients with coreset

• grad_norm — Select by gradient magnitude

Ensemble — Combine multiple selectors:

• stacking — Sequential selector refinement

• voting — Majority vote across selectors

• weighted_fusion — Weighted combination of selector scores

Quick Comparison

Selector	Speed	Quality	Memory	Best For
random	★★★	★	★★★	Baselines
herding	★★★	★★★	★★	General use
influence_functions	★	★★★	★	Small models
forgetting_score	★★	★★★	★★	Classification

Usage

unlearner = ErasusUnlearner(
    model=model,
    strategy="gradient_ascent",
    selector="herding",
    device="cuda",
)

result = unlearner.fit(
    forget_data=forget_loader,
    retain_data=retain_loader,
    prune_ratio=0.3,  # Keep 30% as coreset
)