6 papers · 1 filter
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…
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…
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…
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…
GraSS: Scalable Data Attribution with Gradient Sparsification and Sparse Projection
Pingbang Hu, Joseph Melkonian, Weijing Tang +2
Gradient-based data attribution methods, such as influence functions, are critical for understanding the impact of individual training samples without requiring repeated model retr…
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
Junwei Deng, Weijing Tang, Jiaqi W. Ma
Influence function, a technique rooted in robust statistics, has been adapted in modern machine learning for a novel application: data attribution -- quantifying how individual tra…