2 citations · 3 across the 8 of their papers we have counts for
9 papers
dattri-LLM: A Unified and Efficient Library for Training Data Attribution at LLM Scale
Shixuan Liu, Tongli Zhou, Junwei Deng +2
Training data attribution (TDA) estimates the contribution of individual training examples to model outputs. Most scalable TDA methods rely on per-example gradients, whose computat…
What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music
Luyang Zhang, Xirui Jiang, Junwei Deng +3
Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings. This rais…
How Faithful Is Trajectory-Based Data Attribution? Error Sources, Remedies, and Practical Guidelines
Junwei Deng, Pingbang Hu, Suliang Jin +4
Trajectory-based data attribution methods estimate the influence of training samples on model predictions by unrolling the training trajectory. They are widely used in applications…
Exploring Training Data Attribution under Limited Access Constraints
Shiyuan Zhang, Junwei Deng, Juhan Bae +1
Training data attribution (TDA) plays a critical role in understanding the influence of individual training data points on model predictions. Gradient-based TDA methods, popularize…
Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining
Weiyi Wang, Junwei Deng, Yuzheng Hu +5
Data attribution methods, which quantify the influence of individual training data points on a machine learning model, have gained increasing popularity in data-centric application…
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…
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…
Computational Copyright: Towards A Royalty Model for Music Generative AI
Junwei Deng, Xirui Jiang, Shiyuan Zhang +5
The rapid rise of generative AI has intensified copyright and economic tensions in creative industries, particularly in music. Current approaches addressing this challenge often fo…