The Shapley value has been extensively used in many fields as the unique metric to fairly evaluate player contributions in cooperative settings. Since the exact computation of Shapley values is \#P-hard in the task-agnostic setting, many studies have been developed to utilize the Monte Carlo method for Shapley value estimation. The existing methods estimate the Shapley values directly. In this paper, we explore a novel idea-inferring the Shapley values by estimating the differences between them. Technically, we estimate a differential matrix consisting of pairwise Shapley value differences to reduce the variance of the estimated Shapley values. We develop a least-squares optimization solution to derive the Shapley values from the differential matrix, minimizing the estimator variances. Additionally, we devise a Monte Carlo method for efficient estimation of the differential matrix and introduce two stratified Monte Carlo methods for further variance reduction. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the differential-matrix-based sampling approaches.

Watermarking is broadly utilized to protect ownership of shared data while preserving data utility. However, existing watermarking methods for tabular datasets fall short on the desired properties (detectability, non-intrusiveness, and robustness) and only preserve data utility from the perspective of data statistics, ignoring the performance of downstream ML models trained on the datasets. Can we watermark tabular datasets without significantly compromising their utility for training ML models while preventing attackers from training usable ML models on attacked datasets?

The Shapley value provides a unique approach to equitably gauge each player's contribution within a coalition and has extensive applications with various utility functions. In data valuation for machine learning, particularly for classification tasks, using classification accuracy as the utility function has become a de facto standard. However, accuracy can be an imprecise metric, potentially missing finer details crucial for valuation. In this paper, we propose the probability-based Shapley (P-Shapley) value, which leverages predicted probabilities to heighten utility differentiation. Several convex calibration functions are further incorporated for probability calibration. We prove that the P-Shapley value outperforms Shapley values based on accuracy or other coarse metrics in approximation stability and the discrimination of marginal utility change can be further improved by convex calibration functions. Extensive experiments on four real-world datasets demonstrate the effectiveness of our approaches.