arXiv:2606.05919v1 Announce Type: cross Abstract: Identifying most influential sets (MIS) - size-$k$ subsets whose removal maximally changes a target estimand - is typically infeasible because it requires searching over $\binom{n}{k}$ subsets. For estimands with linear-fractional leave-set-out effects, we show that MIS selection reduces to a one-parameter sequence of top-$k$ problems. Dinkelbach's method yields an algorithm with $\mathcal{O}(n)$ cost per iteration and finite termination. For fixed residualized inputs, the algorithm returns a globally optimal set for the univariate ratio object
The paper tackles a fundamental computational challenge with a novel mathematical approach, leveraging Dinkelbach's method for efficient identification of influential subsets.
This research provides a more efficient method for understanding the true impact of specific data subsets, which is critical for robust model building and decision-making across various AI and statistical applications.
The ability to identify 'most influential sets' with dramatically reduced computational cost changes how researchers and practitioners can audit, optimize, and ensure the reliability of complex AI systems.
- · AI researchers
- · Data scientists
- · Machine learning engineers
- · Sectors reliant on robust model auditing
- · Inefficient brute-force methods
Faster and more accurate identification of critical data points affecting model outcomes.
Improved model explainability and reduction of hidden biases through better understanding of data influence.
Enhanced trust and broader adoption of AI systems due to greater transparency and reliability.
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Read at arXiv cs.LG