12 papers
Deep Barycentric Regression for Optimal Transport Map Estimation and its Statistical Optimality
Kunwoong Kim, Insung Kong, Yongdai Kim
The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learning. However, existing estima…
COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering
Kyungseon Lee, Hankyo Jeong, Kunwoong Kim +2
Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgr…
Doubly-Regressing Approach for Subgroup Fairness
Kunwoong Kim, Kyungseon Lee, Jihu Lee +2
Algorithmic fairness is a socially crucial topic in real-world applications of AI. Among many notions of fairness, subgroup fairness is widely studied when multiple sensitive attri…
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 Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that…
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
Dongyoon Yang, Jihu Lee, Yongdai Kim
Robust domain adaptation against adversarial attacks is a critical research area that aims to develop models capable of maintaining consistent performance across diverse and challe…
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,…
Fair Clustering via Alignment
Kunwoong Kim, Jihu Lee, Sangchul Park +1
Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute. While recently developed fair…
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…
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…
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights
Insung Kong, Yongdai Kim
Bayesian approaches for training deep neural networks (BNNs) have received significant interest and have been effectively utilized in a wide range of applications. There have been…