Search Results for author: Yongchan Kwon

Found 15 papers, 8 papers with code

Data Acquisition: A New Frontier in Data-centric AI

no code implementations22 Nov 2023 Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou

As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative.

DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

1 code implementation2 Oct 2023 Yongchan Kwon, Eric Wu, Kevin Wu, James Zou

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.

Influence Approximation

Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations

1 code implementation4 May 2023 Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou

Our work highlights the importance of understanding the nonlinear effects of model improvement on performance in different subpopulations, and has the potential to inform the development of more equitable and responsible machine learning models.


Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value

2 code implementations16 Apr 2023 Yongchan Kwon, James Zou

As a result, it has been recognized as infeasible to apply to large datasets.

Data Valuation

WeightedSHAP: analyzing and improving Shapley based feature attributions

1 code implementation27 Sep 2022 Yongchan Kwon, James Zou

On several real-world datasets, we demonstrate that the influential features identified by WeightedSHAP are better able to recapitulate the model's predictions compared to the features identified by the Shapley value.

Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning

2 code implementations3 Mar 2022 Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Zou

Our systematic analysis demonstrates that this gap is caused by a combination of model initialization and contrastive learning optimization.

Contrastive Learning Fairness +2

Competition over data: how does data purchase affect users?

no code implementations26 Jan 2022 Yongchan Kwon, Antonio Ginart, James Zou

We introduce a new environment that allows ML predictors to use active learning algorithms to purchase labeled data within their budgets while competing against each other to attract users.

Active Learning

Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

2 code implementations26 Oct 2021 Yongchan Kwon, James Zou

Data Shapley has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning.

BIG-bench Machine Learning Data Valuation

Competing AI: How does competition feedback affect machine learning?

no code implementations15 Sep 2020 Antonio Ginart, Eva Zhang, Yongchan Kwon, James Zou

A service that is more often queried by users, perhaps because it more accurately anticipates user preferences, is also more likely to obtain additional user data (e. g. in the form of a Yelp review).

BIG-bench Machine Learning

Efficient computation and analysis of distributional Shapley values

no code implementations2 Jul 2020 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.

Binary Classification Density Estimation

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric

1 code implementation28 Jan 2019 Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik

We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning).

Hyperparameter Optimization

Uncertainty quantification of molecular property prediction using Bayesian neural network models

no code implementations19 Nov 2018 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 chemical reactions.

Molecular Property Prediction Property Prediction +1

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