39 citations · 105 across the 33 of their papers we have counts for
7 papers · 1 filter
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…
: A Library for Efficient Data Attribution
Junwei Deng, Ting-Wei Li, Shiyuan Zhang +7
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models. As training data plays an increasing…
Most Influential Subset Selection: Challenges, Promises, and Beyond
Yuzheng Hu, Pingbang Hu, Han Zhao +1
How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often…
Adversarial Attacks on Data Attribution
Xinhe Wang, Pingbang Hu, Junwei Deng +1
Data attribution aims to quantify the contribution of individual training data points to the outputs of an AI model, which has been used to measure the value of training data and c…
DCA-Bench: A Benchmark for Dataset Curation Agents
Benhao Huang, Yingzhuo Yu, Jin Huang +2
The quality of datasets plays an increasingly crucial role in the research and development of modern artificial intelligence (AI). Despite the proliferation of open dataset platfor…
Efficient Ensembles Improve Training Data Attribution
Junwei Deng, Ting-Wei Li, Shichang Zhang +1
Training data attribution (TDA) methods aim to quantify the influence of individual training data points on the model predictions, with broad applications in data-centric AI, such…