4 citations · 10 across the 14 of their papers we have counts for
6 papers · 1 filter
Bayesian Neural Networks for Functional ANOVA model
Seokhun Park, Choeun Kim, Jihu Lee +3
With the increasing demand for interpretability in machine learning, functional ANOVA decomposition has gained renewed attention as a principled tool for breaking down high-dimensi…
Bayesian Additive Regression Trees for functional ANOVA model
Seokhun Park, Insung Kong, Yongdai Kim
Bayesian Additive Regression Trees (BART) is a powerful statistical model that leverages the strengths of Bayesian inference and regression trees. It has received significant atten…
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics
Insung Kong, Kunwoong Kim, Yongdai Kim
AI fairness, also known as algorithmic fairness, aims to ensure that algorithms operate without bias or discrimination towards any individual or group. Among various AI algorithms,…
ReLU integral probability metric and its applications
Yuha Park, Kunwoong Kim, Insung Kong +1
We propose a parametric integral probability metric (IPM) to measure the discrepancy between two probability measures. The proposed IPM leverages a specific parametric family of di…
Tensor Product Neural Networks for Functional ANOVA Model
Seokhun Park, Insung Kong, Yongchan Choi +2
Interpretability for machine learning models is becoming more and more important as machine learning models become more complex. The functional ANOVA model, which decomposes a high…
Fairness Through Matching
Kunwoong Kim, Insung Kong, Jongjin Lee +3
Group fairness requires that different protected groups, characterized by a given sensitive attribute, receive equal outcomes overall. Typically, the level of group fairness is mea…