Author name cluster

Jong-June Jeon

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Impute Missing Entries with Uncertainty

Jaesung Lim
Seunghwan An
Jong-June Jeon

Missing data presents a widespread challenge in real-world data collection. In this paper, our goal is to impute missing entries while accurately reflecting the uncertainty associated with them. We introduce U-VAE, a method that employs a non-parametric distributional learning strategy to parameterize the likelihood of missing values. To address the infeasibility of directly estimating the underlying conditional distributions due to data incompleteness, we incorporate stochastic re-masking and un-masking techniques during training. Specifically, we replace the conventional reconstruction loss with the continuous ranked probability score (CRPS), a strictly proper scoring rule, and theoretically demonstrate that the discrepancy between the underlying conditional distribution and our imputer is upper-bounded. We evaluate the performance of U-VAE on 11 real-world datasets, showing its effectiveness in both single and multiple imputations, while also enhancing post-imputation performance and supporting valid statistical inference.

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IJCAI Conference 2025 Conference Paper

Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting

Kijeong Park
Sungchul Hong
Jong-June Jeon

In stock price forecasting, modeling the probabilistic dependence between stock prices within a time-series framework has remained a persistent and highly challenging area of research. We propose a novel model to explain the extreme co-movement in multivariate data with time-series dependencies. Our model incorporates a Hawkes process layer to capture abrupt co-movements, thereby enhancing the temporal representation of market dynamics. We introduce dynamic hypergraphs into our model adapting to higher-order (groupwise rather than pairwise) relationships within the stock market. Extensive experiments on real-world benchmarks demonstrate the robustness of our approach in predictive performance and portfolio stability.

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AAAI Conference 2025 Conference Paper

Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis

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). Since the MLu performance depends on accurately approximating the conditional distributions, we focus on devising a synthetic data generation method based on conditional distribution estimation. We introduce MaCoDE by redefining the consecutive multi-class classification task of Masked Language Modeling (MLM) as histogram-based non-parametric conditional density estimation. Our approach enables the estimation of conditional densities across arbitrary combinations of target and conditional variables. We bridge the theoretical gap between distributional learning and MLM by demonstrating that minimizing the orderless multi-class classification loss leads to minimizing the total variation distance between conditional distributions. To validate our proposed model, we evaluate its performance in synthetic data generation across 10 real-world datasets, demonstrating its ability to adjust data privacy levels easily without re-training. Additionally, since masked input tokens in MLM are analogous to missing data, we further assess its effectiveness in handling training datasets with missing values, including multiple imputations of the missing entries.

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ECAI Conference 2023 Conference Paper

Causally Disentangled Generative Variational AutoEncoder

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. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings.

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NeurIPS Conference 2023 Conference Paper

Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation

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. In this paper, we propose a new approach that expands the model capacity (i. e. , expressive power of distributional family) without sacrificing the computational advantages of the VAE framework. Our VAE model's decoder is composed of an infinite mixture of asymmetric Laplace distribution, which possesses general distribution fitting capabilities for continuous variables. Our model is represented by a special form of a nonparametric M-estimator for estimating general quantile functions, and we theoretically establish the relevance between the proposed model and quantile estimation. We apply the proposed model to synthetic data generation, and particularly, our model demonstrates superiority in easily adjusting the level of data privacy.

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NeurIPS Conference 2023 Conference Paper

Geodesic Multi-Modal Mixup for Robust Fine-Tuning

Changdae Oh
Junhyuk So
Hoyoon Byun
Yongtaek Lim
Minchul Shin
Jong-June Jeon
Kyungwoo Song

Pre-trained multi-modal models, such as CLIP, provide transferable embeddings and show promising results in diverse applications. However, the analysis of learned multi-modal embeddings is relatively unexplored, and the embedding transferability can be improved. In this work, we observe that CLIP holds separated embedding subspaces for two different modalities, and then we investigate it through the lens of \textit{uniformity-alignment} to measure the quality of learned representation. Both theoretically and empirically, we show that CLIP retains poor uniformity and alignment even after fine-tuning. Such a lack of alignment and uniformity might restrict the transferability and robustness of embeddings. To this end, we devise a new fine-tuning method for robust representation equipping better alignment and uniformity. First, we propose a \textit{Geodesic Multi-Modal Mixup} that mixes the embeddings of image and text to generate hard negative samples on the hypersphere. Then, we fine-tune the model on hard negatives as well as original negatives and positives with contrastive loss. Based on the theoretical analysis about hardness guarantee and limiting behavior, we justify the use of our method. Extensive experiments on retrieval, calibration, few- or zero-shot classification (under distribution shift), embedding arithmetic, and image captioning further show that our method provides transferable representations, enabling robust model adaptation on diverse tasks.

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JMLR Journal 2021 Journal Article

Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression

Gunwoong Park
Sang Jun Moon
Sion Park
Jong-June Jeon

This paper develops a new approach to learning high-dimensional linear structural equation models (SEMs) without the commonly assumed faithfulness, Gaussian error distribution, and equal error distribution conditions. A key component of the algorithm is component-wise ordering and parent estimations, where both problems can be efficiently addressed using l1-regularized regression. This paper proves that sample sizes n = Omega( d^{2} \log p) and n = \Omega( d^2 p^{2/m} ) are sufficient for the proposed algorithm to recover linear SEMs with sub-Gaussian and (4m)-th bounded-moment error distributions, respectively, where p is the number of nodes and d is the maximum degree of the moralized graph. Further shown is the worst-case computational complexity O(n (p^3 + p^2 d^2 ) ), and hence, the proposed algorithm is statistically consistent and computationally feasible for learning a high-dimensional linear SEM when its moralized graph is sparse. Through simulations, we verify that the proposed algorithm is statistically consistent and computationally feasible, and it performs well compared to the state-of-the-art US, GDS, LISTEN and TD algorithms with our settings. We also demonstrate through real COVID-19 data that the proposed algorithm is well-suited to estimating a virus-spread map in China. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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