10 citations · 45 across the 25 of their papers we have counts for
7 papers · 1 filter
Capturing the Temporal Dependence of Training Data Influence
Jiachen T. Wang, Dawn Song, James Zou +2
Traditional data influence estimation methods, like influence function, assume that learning algorithms are permutation-invariant with respect to training data. However, modern tra…
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
Myeongseob Ko, Henry Li, Zhun Wang +6
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful o…
A Survey on Data Markets
Jiayao Zhang, Yuran Bi, Mengye Cheng +15
Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the…
Data Shapley in One Training Run
Jiachen T. Wang, Prateek Mittal, Dawn Song +1
Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. However, existing approaches require re-training models on differ…
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits
Jiachen T. Wang, Tianji Yang, James Zou +2
Data Shapley provides a principled approach to data valuation and plays a crucial role in data-centric machine learning (ML) research. Data selection is considered a standard appli…
An Economic Solution to Copyright Challenges of Generative AI
Jiachen T. Wang, Zhun Deng, Hiroaki Chiba-Okabe +2
Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media. There is growing concern that suc…
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms
Jiachen T. Wang, Prateek Mittal, Ruoxi Jia
This work aims to address an open problem in data valuation literature concerning the efficient computation of Data Shapley for weighted nearest neighbor algorithm (WKNN-Shaple…