100 citations · 192 across the 13 of their papers we have counts for
8 papers · 1 filter
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Yongchan Kwon, Eric Wu, Kevin Wu +1
Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influe…
OpenDataVal: a Unified Benchmark for Data Valuation
Kevin Fu Jiang, Weixin Liang, James Zou +1
Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data v…
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value
Yongchan Kwon, James Zou
Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training. Many Shapley-based data valuation methods…
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Weixin Liang, Yining Mao, Yongchan Kwon +2
Understanding the performance of machine learning (ML) models across diverse data distributions is critically important for reliable applications. Despite recent empirical studies…
WeightedSHAP: analyzing and improving Shapley based feature attributions
Yongchan Kwon, James Zou
Shapley value is a popular approach for measuring the influence of individual features. While Shapley feature attribution is built upon desiderata from game theory, some of its con…
Competition over data: how does data purchase affect users?
Yongchan Kwon, Antonio Ginart, James Zou
As machine learning (ML) is deployed by many competing service providers, the underlying ML predictors also compete against each other, and it is increasingly important to understa…
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Yongchan Kwon, James Zou
Data Shapley has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. It can effectively identify helpful or harmf…
Uncertainty quantification of molecular property prediction with Bayesian neural networks
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisi…