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Daeho Um

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7 papers

2 author rows

ICML Conference 2025 Conference Paper

PRIME: Deep Imbalanced Regression with Proxies

Jongin Lim 0002
Sucheol Lee
Daeho Um
Sung-Un Park
Jinwoo Shin

Data imbalance remains a fundamental challenge in real-world machine learning. However, most existing work has focused on classification, leaving imbalanced regression underexplored despite its importance in many applications. To address this gap, we propose PRIME, a framework that leverages learnable proxies to construct a balanced and well-ordered feature space for imbalanced regression. At its core, PRIME arranges proxies to be uniformly distributed in the feature space while preserving the ordinal structure of regression targets, and then aligns each sample feature to its corresponding proxy. By using proxies as reference points, PRIME induces the desired structure of learned representations, promoting better generalization, especially in underrepresented target regions. Moreover, since proxy-based alignment resembles classification, PRIME enables the seamless application of class imbalance techniques to regression, facilitating more balanced feature learning. Extensive experiments demonstrate the effectiveness and broad applicability of PRIME, achieving state-of-the-art performance on four real-world regression benchmark datasets across diverse target domains.

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ICML Conference 2025 Conference Paper

Propagate and Inject: Revisiting Propagation-Based Feature Imputation for Graphs with Partially Observed Features

Daeho Um
Sunoh Kim
Jiwoong Park
Jongin Lim 0002
Seong-Jin Ahn 0002
Seulki Park

In this paper, we address learning tasks on graphs with missing features, enhancing the applicability of graph neural networks to real-world graph-structured data. We identify a critical limitation of existing imputation methods based on feature propagation: they produce channels with nearly identical values within each channel, and these low-variance channels contribute very little to performance in graph learning tasks. To overcome this issue, we introduce synthetic features that target the root cause of low-variance channel production, thereby increasing variance in these channels. By preventing propagation-based imputation methods from generating meaningless feature values shared across all nodes, our synthetic feature propagation scheme mitigates significant performance degradation, even under extreme missing rates. Extensive experiments demonstrate the effectiveness of our approach across various graph learning tasks with missing features, ranging from low to extremely high missing rates. Additionally, we provide both empirical evidence and theoretical proof to validate the low-variance problem. The source code is available at https: //github. com/daehoum1/fisf.

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ICLR Conference 2025 Conference Paper

Relation-Aware Diffusion for Heterogeneous Graphs with Partially Observed Features

Daeho Um
Yoonji Lee
Jiwoong Park
Seulki Park
Yuneil Yeo
Seong-Jin Ahn 0002

Diffusion-based imputation methods, which impute missing features through the iterative propagation of observed features, have shown impressive performance in homogeneous graphs. However, these methods are not directly applicable to heterogeneous graphs, which have multiple types of nodes and edges, due to two key issues: (1) the presence of nodes with undefined features hinders diffusion-based imputation; (2) treating various edge types equally during diffusion does not fully utilize information contained in heterogeneous graphs. To address these challenges, this paper presents a novel imputation scheme that enables diffusion-based imputation in heterogeneous graphs. Our key idea involves (1) assigning a {\it virtual feature} to an undefined node feature and (2) determining the importance of each edge type during diffusion according to a new criterion. Through experiments, we demonstrate that our virtual feature scheme effectively serves as a bridge between existing diffusion-based methods and heterogeneous graphs, maintaining the advantages of these methods. Furthermore, we confirm that adjusting the importance of each edge type leads to significant performance gains on heterogeneous graphs. Extensive experimental results demonstrate the superiority of our scheme in both semi-supervised node classification and link prediction tasks on heterogeneous graphs with missing rates ranging from low to exceedingly high. The source code is available at https://github.com/daehoum1/hetgfd.

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ICLR Conference 2025 Conference Paper

Spreading Out-of-Distribution Detection on Graphs

Daeho Um
Jongin Lim 0002
Sunoh Kim
Yuneil Yeo
Yoonho Jung

Node-level out-of-distribution (OOD) detection on graphs has received significant attention from the machine learning community. However, previous approaches are evaluated using unrealistic benchmarks that consider only randomly selected OOD nodes, failing to reflect the interactions among nodes. In this paper, we introduce a new challenging task to model the interactions of OOD nodes in a graph, termed spreading OOD detection, where a newly emerged OOD node spreads its property to neighboring nodes. We curate realistic benchmarks by employing the epidemic spreading models that simulate the spreading of OOD nodes on the graph. We also showcase a ``Spreading COVID-19" dataset to demonstrate the applicability of spreading OOD detection in real-world scenarios. Furthermore, to effectively detect spreading OOD samples under the proposed benchmark setup, we present a new approach called energy distribution-based detector (EDBD), which includes a novel energy-aggregation scheme. EDBD is designed to mitigate undesired mixing of OOD scores between in-distribution (ID) and OOD nodes. Our extensive experimental results demonstrate the superiority of our approach over state-of-the-art methods in both spreading OOD detection and conventional node-level OOD detection tasks across seven benchmark datasets. The source code is available at https://github.com/daehoum1/edbd.

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NeurIPS Conference 2024 Conference Paper

Gene-Gene Relationship Modeling Based on Genetic Evidence for Single-Cell RNA-Seq Data Imputation

Daeho Um
Ji Won Yoon
Seong Jin Ahn
Yunha Yeo

Single-cell RNA sequencing (scRNA-seq) technologies enable the exploration of cellular heterogeneity and facilitate the construction of cell atlases. However, scRNA-seq data often contain a large portion of missing values (false zeros) or noisy values, hindering downstream analyses. To recover these false zeros, propagation-based imputation methods have been proposed using $k$-NN graphs. However they model only associating relationships among genes within a cell, while, according to well-known genetic evidence, there are both associating and dissociating relationships among genes. To apply this genetic evidence to gene-gene relationship modeling, this paper proposes a novel imputation method that newly employs dissociating relationships in addition to associating relationships. Our method constructs a $k$-NN graph to additionally model dissociating relationships via the negation of a given cell-gene matrix. Moreover, our method standardizes the value distribution (mean and variance) of each gene to have standard distributions regardless of the gene. Through extensive experiments, we demonstrate that the proposed method achieves exceptional performance gains over state-of-the-art methods in both cell clustering and gene expression recovery across six scRNA-seq datasets, validating the significance of using complete gene-gene relationships in accordance with genetic evidence. The source code is available at https: //github. com/daehoum1/scCR.

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ICLR Conference 2023 Conference Paper

Confidence-Based Feature Imputation for Graphs with Partially Known Features

Daeho Um
Jiwoong Park
Seulki Park
Jin Young Choi 0002

This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. However, in cases of high rates of missing features, they were unable to avoid significant performance degradation. To overcome this limitation, we introduce a novel concept of channel-wise confidence in a node feature, which is assigned to each imputed channel feature of a node for reflecting the certainty of the imputation. We then design pseudo-confidence using the channel-wise shortest path distance between a missing-feature node and its nearest known-feature node to replace unavailable true confidence in an actual learning process. Based on the pseudo-confidence, we propose a novel feature imputation scheme that performs channel-wise inter-node diffusion and node-wise inter-channel propagation. The scheme can endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves state-of-the-art accuracy for both semi-supervised node classification and link prediction on various datasets containing a high rate of missing features. Codes are available at https://github.com/daehoum1/pcfi.

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AAAI Conference 2021 Conference Paper

Class-Attentive Diffusion Network for Semi-Supervised Classification

Jongin Lim
Daeho Um
Hyung Jin Chang
Dae Ung Jo
Jin Young Choi

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to interclass nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https: //github. com/ljin0429/CAD-Net.

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