An Odd Estimator for Shapley Values
Two new research papers propose novel methods for approximating Shapley values, a crucial metric for interpreting machine learning models. The first paper, "ShaplEIG," introduces a Bayesian experimental design approach that uses Gaussian processes to adaptively select coalitions for evaluation, improving sample efficiency in low-budget scenarios. The second paper, "An Odd Estimator for Shapley Values," reveals that Shapley values depend only on the odd component of set functions and proposes a new estimator, OddSHAP, which achieves state-of-the-art accuracy at larger sampling budgets by focusing on this odd subspace. AI
IMPACT These novel methods for Shapley value approximation could enhance the interpretability of complex machine learning models, particularly in resource-constrained settings.