6 papers
Priority-Aware Shapley Value
Kiljae Lee, Ziqi Liu, Weijing Tang +1
Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable. This can be problematic when…
Generalized Priority-Aware Shapley Value
Kiljae Lee, Ziqi Liu, Weijing Tang +1
Shapley value and its priority-aware extensions are widely used for valuation in machine learning, but existing methods require pairwise priority to be binary and acyclic, a restri…
First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
Ziqi Liu, Kiljae Lee, Yuan Zhang +1
Probabilistic values, including Shapley values and semivalues, provide a model-agnostic framework to attribute the behavior of a black-box model to data points or features, with a…
Representation Learning with Blockwise Missingness and Signal Heterogeneity
Ziqi Liu, Ye Tian, Weijing Tang
Unified representation learning for multi-source data integration faces two important challenges: blockwise missingness and blockwise signal heterogeneity. The former arises from s…
Faithful Group Shapley Value
Kiljae Lee, Ziqi Liu, Weijing Tang +1
Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models. In practice, group-level data valuatio…
Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution
Yiwen Tu, Ziqi Liu, Jiaqi W. Ma +1
Measuring task relatedness and mitigating negative transfer remain a critical open challenge in Multitask Learning (MTL). This work extends data attribution -- which quantifies the…