Search Results for author: Jong-June Jeon

Found 13 papers, 5 papers with code

Does a Large Language Model Really Speak in Human-Like Language?

no code implementations2 Jan 2025 Mose Park, Yunjin Choi, Jong-June Jeon

Our analysis addresses two key questions: (1) Is the difference in latent community structures between $\mathcal{O}$ and $\mathcal{G}$ the same as that between $\mathcal{G}$ and $\mathcal{S}$?

Language Modeling Language Modelling +2

Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis

no code implementations31 May 2024 SeungHwan An, Gyeongdong Woo, Jaesung Lim, Changhyun Kim, Sungchul Hong, Jong-June Jeon

In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu).

Density Estimation Imputation +6

Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering

no code implementations30 May 2024 Sungchul Hong, SeungHwan An, Jong-June Jeon

We investigate the problem of the generative model for imbalanced classification and introduce a framework to enhance the SMOTE algorithm using Variational Autoencoders (VAE).

Classification Data Augmentation +1

Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance

no code implementations6 Dec 2023 SeungHwan An, Sungchul Hong, Jong-June Jeon

This measure enables us to capture both marginal and joint distributional information simultaneously, as it incorporates a mixture measure with point masses on standard basis vectors.

Synthetic Data Generation

Joint Distributional Learning via Cramer-Wold Distance

no code implementations25 Oct 2023 SeungHwan An, Jong-June Jeon

The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-dimensional datasets or complex correlation structures among observed variables.

Decoder Synthetic Data Generation

Uniform Pessimistic Risk and its Optimal Portfolio

no code implementations2 Mar 2023 Sungchul Hong, Jong-June Jeon

However, estimating an optimal portfolio assessed by a pessimistic risk is still challenging due to the absence of a computationally tractable model.

quantile regression scoring rule

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

no code implementations28 Feb 2023 Sunghcul Hong, Yunjin Choi, Jong-June Jeon

Accurate forecasting of river water levels is vital for effectively managing traffic flow and mitigating the risks associated with natural disasters.

Time Series Analysis

Causally Disentangled Generative Variational AutoEncoder

1 code implementation23 Feb 2023 SeungHwan An, Kyungwoo Song, Jong-June Jeon

We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously.

Disentanglement

Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation

1 code implementation NeurIPS 2023 SeungHwan An, Jong-June Jeon

The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE) despite its efficiency in computational modeling.

Decoder Synthetic Data Generation

EXoN: EXplainable encoder Network

1 code implementation23 May 2021 SeungHwan An, Hosik Choi, Jong-June Jeon

To improve the performance of our VAE in a classification task without the loss of performance as a generative model, we employ a new semi-supervised classification method called SCI (Soft-label Consistency Interpolation).

Classification

Primal path algorithm for compositional data analysis

no code implementations21 Dec 2018 Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi

To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used.

General Classification regression

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