100 citations · 192 across the 13 of their papers we have counts for
13 papers
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…
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
Weixin Liang, Yuhui Zhang, Yongchan Kwon +2
We present modality gap, an intriguing geometric phenomenon of the representation space of multi-modal models. Specifically, we show that different data modalities (e.g. images and…
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…
Efficient computation and analysis of distributional Shapley values
Yongchan Kwon, Manuel A. Rivas, James Zou
Distributional data Shapley value (DShapley) has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. DShapley dev…
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won +1
Wasserstein distributionally robust optimization (WDRO) attempts to learn a model that minimizes the local worst-case risk in the vicinity of the empirical data distribution define…
Uncertainty quantification of molecular property prediction using Bayesian neural network models
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning c…
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…
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama +1
We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior perfor…