6 papers · 1 filter
From Detrimental to Beneficial: Dynamic Influence-based Valuation and Editing
Adrian Nyakairu, Hongfu Liu
Data valuation is a cornerstone of data-centric learning, where prior efforts primarily focus on designing algorithms to classify training samples as either beneficial or detriment…
Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation
Yinan Shen, Ziao Yang, Hongfu Liu
Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing practice often assumes these va…
Layer-Aware Influence for Online Data Valuation Estimation
Ziao Yang, Longbo Huang, Hongfu Liu
Data-centric learning emphasizes curating high-quality training samples to boost performance rather than designing new architectures. A central problem is to estimate the influence…
On the Inflation of KNN-Shapley Value
Ziao Yang, Han Yue, Jian Chen +1
Shapley value-based data valuation methods, originating from cooperative game theory, quantify the usefulness of each individual sample by considering its contribution to all possi…
Revisit, Extend, and Enhance Hessian-Free Influence Functions
Ziao Yang, Han Yue, Jian Chen +1
Influence functions serve as crucial tools for assessing sample influence in model interpretation, subset training set selection, noisy label detection, and more. By employing the…
Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models
Anshuman Chhabra, Bo Li, Jian Chen +2
A core data-centric learning challenge is the identification of training samples that are detrimental to model performance. Influence functions serve as a prominent tool for this t…