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Munich Center for Machine Learning, LMU Munich
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Adaptive coalition selection in ShaplEIG boosts Shapley value estimation efficiency, slashing computational complexity and enhancing performance in resource-constrained settings.
ProxySHAP slashes the computational cost of Shapley interaction estimation while simultaneously boosting accuracy, finally making high-order interaction analysis practical for models with thousands of features.
Credal sets, previously impractical for large models, are now efficiently computable via a "decalibration" method that delivers strong performance in uncertainty-aware tasks.