  The Shapley value (SV) and Least core (LC) are classic methods in cooperative
game theory for cost/profit sharing problems. Both methods have recently been
proposed as a principled solution for data valuation tasks, i.e., quantifying
the contribution of individual datum in machine learning. However, both SV and
LC suffer computational challenges due to the need for retraining models on
combinatorially many data subsets. In this work, we propose to boost the
efficiency in computing Shapley value or Least core by learning to estimate the
performance of a learning algorithm on unseen data combinations. Theoretically,
we derive bounds relating the error in the predicted learning performance to
the approximation error in SV and LC. Empirically, we show that the proposed
method can significantly improve the accuracy of SV and LC estimation.
