Abstract: The family of probabilistic values, axiomatically-grounded and proposed in cooperative game theory, has recently received much attention in data valuation. However, it is often computationally expensive to compute exactly (exponential w.r.t. $N$, the number of data being valuated). Existing generic estimators cost $O(\frac{N^2}{\epsilon^2}\log\frac{N}{\delta})$ utility evaluations to achieve an ($\epsilon$, $\delta$)-approximation under the 2-norm, while faster estimators have been developed recently for special cases (e.g., empirically for the Shapley value and theoretically for the Banzhaf value). In this work, based on a connection between probabilistic values and least square regressions, we propose two generic estimators for the whole family of probabilistic values that both cost $O(\frac{N}{\epsilon^2}\log\frac{N}{\delta})$ utility evaluations, largely extending the scope of this currently best complexity bound. Moreover, we show that each distributional value, proposed by Ghorbani et al. (2020) to alleviate the inconsistency of probabilistic values when using distinct databases, can also be cast as optimizing a similar least square regression. This observation makes it the first-time theoretically-grounded to train value estimators such that the distributional value of each unseen data point can be evaluated in a single forward pass. Our experiments verify the faster convergence of our proposed estimators, and demonstrate the effectiveness at learning distributional values.