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name: aggregate_influence_pruning description: Select removable data subsets by constraining aggregate influence vector norm instead of ranking independent scalar scores.

Aggregate Influence Pruning

Use this skill after per-example influence vectors have been estimated. It implements the paper's central subset-selection idea: maximize removable cardinality while the norm of the sum of removed influences remains below epsilon.

Inputs

• Per-example influence vectors with equal dimensionality.
• epsilon for generalization-guaranteed pruning or cardinality for fixed-size pruning.
• Bounded search mode; exhaustive search is appropriate only for small recovery cases.

Outputs

• Selected removal indices and binary mask.
• Aggregate influence vector and norm.
• Feasibility and objective metadata.

Workflow

1. Validate influence vector dimensions and finite values.
2. Enumerate candidate masks for tiny recovery runs, or use a deterministic bounded heuristic for larger runs.
3. Prefer larger feasible subsets; break ties by lower aggregate norm.
4. Save enough metadata to prove joint cancellation was considered.

Validation

Run python scripts/prune_by_influence.py --demo and python tests/test_prune_by_influence.py from this skill directory.

Limitations

The exhaustive implementation is for bounded recovery and testing. Large datasets should use a time-limited heuristic such as simulated annealing while preserving the same objective contract.