8 citations · 29 across the 10 of their papers we have counts for
6 papers · 1 filter
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
Hyemi Kim, Seungjae Shin, JoonHo Jang +4
The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating th…
Sequential Likelihood-Free Inference with Neural Proposal
Dongjun Kim, Kyungwoo Song, YoonYeong Kim +4
Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulat…
The layer number of -evenly distributed point sets
Ilkyoo Choi, Weonyoung Joo, Minki Kim
For a finite point set in , we consider a peeling process where the vertices of the convex hull are removed at each step. The layer number of a given point set…
Adversarial Likelihood-Free Inference on Black-Box Generator
Dongjun Kim, Weonyoung Joo, Seungjae Shin +2
Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input p…
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
Seungjae Shin, Kyungwoo Song, JoonHo Jang +3
Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discrimi…
Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables
Weonyoung Joo, Dongjun Kim, Seungjae Shin +1
Estimating the gradients of stochastic nodes in stochastic computational graphs is one of the crucial research questions in the deep generative modeling community, which enables th…