Yongchan Kwon | DeepAI

AI Chat

Featured Co-authors

Masashi Sugiyama
222 publications
James Zou
123 publications
Xinyu Yang
46 publications
Serena Yeung
36 publications
Weixin Liang
19 publications
Yuhui Zhang
14 publications
Myunghee Cho Paik
11 publications
Woo Youn Kim
9 publications
Joong-Ho Won
8 publications
Wonyoung Kim
7 publications
Seongok Ryu
7 publications

∙ 06/18/2023

OpenDataVal: a Unified Benchmark for Data Valuation

Assessing the quality and impact of individual data points is critical f...

0 Kevin Fu Jiang, et al. ∙

∙ 05/04/2023

On the nonlinear correlation of ML performance between data subpopulations

Understanding the performance of machine learning (ML) models across div...

0 Weixin Liang, et al. ∙

∙ 04/16/2023

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

Data valuation is a powerful framework for providing statistical insight...

0 Yongchan Kwon, et al. ∙

∙ 09/27/2022

WeightedSHAP: analyzing and improving Shapley based feature attributions

Shapley value is a popular approach for measuring the influence of indiv...

0 Yongchan Kwon, et al. ∙

∙ 03/03/2022

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

We present modality gap, an intriguing geometric phenomenon of the repre...

15 Weixin Liang, et al. ∙

∙ 01/26/2022

Competition over data: how does data purchase affect users?

As machine learning (ML) is deployed by many competing service providers...

0 Yongchan Kwon, et al. ∙

∙ 10/26/2021

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

Data Shapley has recently been proposed as a principled framework to qua...

0 Yongchan Kwon, et al. ∙

∙ 09/15/2020

Competing AI: How does competition feedback affect machine learning?

This papers studies how competition affects machine learning (ML) predic...

43 Antonio Ginart, et al. ∙

∙ 07/02/2020

Efficient computation and analysis of distributional Shapley values

Distributional data Shapley value (DShapley) has been recently proposed ...

4 Yongchan Kwon, et al. ∙

∙ 06/05/2020

Principled Learning Method for Wasserstein Distributionally Robust Optimization with Local Perturbations

Wasserstein distributionally robust optimization (WDRO) attempts to lear...

0 Yongchan Kwon, et al. ∙

∙ 03/20/2019

Uncertainty quantification of molecular property prediction with Bayesian neural networks

Deep neural networks have outperformed existing machine learning models ...

0 Seongok Ryu, et al. ∙

∙ 01/28/2019

An analytic formulation for positive-unlabeled learning via weighted integral probability metric

We consider the problem of learning a binary classifier from only positi...

0 Yongchan Kwon, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world