5 papers
RL-IF: Tracking Non-local Training Data Influence for Online Reinforcement Learning
Shixuan Liu, Cheng Tang, Yuzheng Hu +3
Online reinforcement learning (RL) has achieved significant success in decision-making tasks, but modern online RL remains highly sensitive to the quality of training experience. U…
Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution
Shixuan Liu, Joan Serrà, Kin Wai Cheuk +6
Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require costly per-sample grad…
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…
MapExplorer: New Content Generation from Low-Dimensional Visualizations
Xingjian Zhang, Ziyang Xiong, Shixuan Liu +6
Low-dimensional visualizations, or "projection maps," are widely used in scientific and creative domains to interpret large-scale and complex datasets. These visualizations not onl…
: 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…