16 citations · 62 across the 20 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…
LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning
Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang +1
Active learning effectively collects data instances for training deep learning models when the labeled dataset is limited and the annotation cost is high. Besides active learning,…
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…
Approximate Inference for Spectral Mixture Kernel
Yohan Jung, Kyungwoo Song, Jinkyoo Park
A spectral mixture (SM) kernel is a flexible kernel used to model any stationary covariance function. Although it is useful in modeling data, the learning of the SM kernel is gener…
Implicit Kernel Attention
Kyungwoo Song, Yohan Jung, Dongjun Kim +1
\textit{Attention} computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models, such as Transf…
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…