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Hwanjo Yu

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12 papers

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AAAI Conference 2026 Conference Paper

Harmonic Dataset Distillation for Time Series Forecasting

Seungha Hong
Sanghwan Jang
Wonbin Kweon
Suyeon Kim
Gyuseok Lee
Hwanjo Yu

Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD), a paradigm that synthesizes a small, compact dataset to achieve training performance comparable to that of the original dataset, has emerged as a promising solution. However, conventional DD methods are not tailored for time series and suffer from architectural overfitting and limited scalability. To address these issues, we propose Harmonic Dataset Distillation for Time Series Forecasting (HDT). HDT decomposes the time series into its sinusoidal basis through the FFT and aligns the core periodic structure by Harmonic Matching. Since this process operates in the frequency domain, all updates during distillation are applied globally without disrupting temporal dependencies of time series. Extensive experiments demonstrate that HDT achieves strong cross-architecture generalization and scalability, validating its practicality for large-scale, real-world applications.

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AAAI Conference 2024 Conference Paper

Multi-Domain Recommendation to Attract Users via Domain Preference Modeling

Hyunjun Ju
SeongKu Kang
Dongha Lee
Junyoung Hwang
Sanghwan Jang
Hwanjo Yu

Recently, web platforms are operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains. In this paper, we point out two challenges of MDRAU task. First, there are numerous possible combinations of mappings from seen to unseen domains because users have usually interacted with a different subset of service domains. Second, a user might have different preference for each of the target unseen domains, which requires recommendations to reflect users' preference on domains as well as items. To tackle these challenges, we propose DRIP framework that models users' preference at two levels (i.e., domain and item) and learns various seen-unseen domain mappings in a unified way with masked domain modeling. Our extensive experiments demonstrate the effectiveness of DRIP in MDRAU task and its ability to capture users' domain-level preferences.

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AAAI Conference 2023 Conference Paper

Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation

Junsu Cho
Dongmin Hyun
Dong won Lim
Hyeon jae Cheon
Hyoung-iel Park
Hwanjo Yu

Sequential Recommender Systems (SRSs) aim to predict the next item that users will consume, by modeling the user interests within their item sequences. While most existing SRSs focus on a single type of user behavior, only a few pay attention to multi-behavior sequences, although they are very common in real-world scenarios. It is challenging to effectively capture the user interests within multi-behavior sequences, because the information about user interests is entangled throughout the sequences in complex relationships. To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics. DyMuS first encodes each behavior sequence independently, and then combines the encoded sequences using dynamic routing, which dynamically integrates information required in the final result from among many candidates, based on correlations between the sequences. DyMuS+, furthermore, applies the dynamic routing even to encoding each behavior sequence to further capture the correlations at item-level. Moreover, we release a new, large and up-to-date dataset for multi-behavior recommendation. Our experiments on DyMuS and DyMuS+ show their superiority and the significance of capturing the characteristics of multi-behavior sequences.

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AAAI Conference 2023 Conference Paper

Learning Topology-Specific Experts for Molecular Property Prediction

Suyeon Kim
Dongha Lee
SeongKu Kang
Seonghyeon Lee
Hwanjo Yu

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.

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AAAI Conference 2022 Conference Paper

Obtaining Calibrated Probabilities with Personalized Ranking Models

Wonbin Kweon
SeongKu Kang
Hwanjo Yu

For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, probability calibration has not been much explored for personalized ranking. In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. We investigate various parametric distributions and propose two parametric calibration methods, namely Gaussian calibration and Gamma calibration. Each proposed method can be seen as a post-processing function that maps the ranking scores of pre-trained models to well-calibrated preference probabilities, without affecting the recommendation performance. We also design the unbiased empirical risk minimization framework that guides the calibration methods to the learning of true preference probability from the biased user-item interaction dataset. Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.

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AAAI Conference 2021 Conference Paper

Learnable Dynamic Temporal Pooling for Time Series Classification

Dongha Lee
Seonghyeon Lee
Hwanjo Yu

With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines. Recent studies on time series classification show that convolutional neural networks (CNN) achieved the state-of-the-art performance as a single classifier. In this work, pointing out that the global pooling layer that is usually adopted by existing CNN classifiers discards the temporal information of high-level features, we present a dynamic temporal pooling (DTP) technique that reduces the temporal size of hidden representations by aggregating the features at the segment-level. For the partition of a whole series into multiple segments, we utilize dynamic time warping (DTW) to align each time point in a temporal order with the prototypical features of the segments, which can be optimized simultaneously with the network parameters of CNN classifiers. The DTP layer combined with a fullyconnected layer helps to extract further discriminative features considering their temporal position within an input time series. Extensive experiments on both univariate and multivariate time series datasets show that our proposed pooling significantly improves the classification performance.

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IJCAI Conference 2020 Conference Paper

Convolutional Neural Networks with Compression Complexity Pooling for Out-of-Distribution Image Detection

Sehun Yu
Dongha Lee
Hwanjo Yu

To reliably detect out-of-distribution images based on already deployed convolutional neural networks, several recent studies on the out-of-distribution detection have tried to define effective confidence scores without retraining the model. Although they have shown promising results, most of them need to find the optimal hyperparameter values by using a few out-of-distribution images, which eventually assumes a specific test distribution and makes it less practical for real-world applications. In this work, we propose a novel out-of-distribution detection method termed as MALCOM, which neither uses any out-of-distribution sample nor retrains the model. Inspired by an observation that the global average pooling cannot capture spatial information of feature maps in convolutional neural networks, our method aims to extract informative sequential patterns from the feature maps. To this end, we introduce a similarity metric that focuses on shared patterns between two sequences based on the normalized compression distance. In short, MALCOM uses both the global average and the spatial patterns of feature maps to identify out-of-distribution images accurately.

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AAAI Conference 2020 Conference Paper

Unsupervised Attributed Multiplex Network Embedding

Chanyoung Park
Donghyun Kim
Jiawei Han
Hwanjo Yu

Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. We devise a systematic way to jointly integrate the node embeddings from multiple graphs by introducing 1) the consensus regularization framework that minimizes the disagreements among the relation-type speciﬁc node embeddings, and 2) the universal discriminator that discriminates true samples regardless of the relation types. We also show that the attention mechanism infers the importance of each relation type, and thus can be useful for ﬁltering unnecessary relation types as a preprocessing step. Extensive experiments on various downstream tasks demonstrate that DMGI outperforms the stateof-the-art methods, even though DMGI is fully unsupervised.

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IJCAI Conference 2019 Conference Paper

Action Space Learning for Heterogeneous User Behavior Prediction

Dongha Lee
Chanyoung Park
Hyunjun Ju
Junyoung Hwang
Hwanjo Yu

Users' behaviors observed in many web-based applications are usually heterogeneous, so modeling their behaviors considering the interplay among multiple types of actions is important. However, recent collaborative filtering (CF) methods based on a metric learning approach cannot learn multiple types of user actions, because they are developed for only a single type of user actions. This paper proposes a novel metric learning method, called METAS, to jointly model heterogeneous user behaviors. Specifically, it learns two distinct spaces: 1) action space which captures the relations among all observed and unobserved actions, and 2) entity space which captures high-level similarities among users and among items. Each action vector in the action space is computed using a non-linear function and its corresponding entity vectors in the entity space. In addition, METAS adopts an efficient triplet mining algorithm to effectively speed up the convergence of metric learning. Experimental results show that METAS outperforms the state-of-the-art methods in predicting users' heterogeneous actions, and its entity space represents the user-user and item-item similarities more clearly than the space trained by the other methods.

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IJCAI Conference 2019 Conference Paper

Sequential and Diverse Recommendation with Long Tail

Yejin Kim
Kwangseob Kim
Chanyoung Park
Hwanjo Yu

Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy. To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction.

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AIIM Journal 2008 Journal Article

Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods

Baek Hwan Cho
Hwanjo Yu
Kwang-Won Kim
Tae Hyun Kim
In Young Kim
Sun I. Kim

Objective Diabetic nephropathy is damage to the kidney caused by diabetes mellitus. It is a common complication and a leading cause of death in people with diabetes. However, the decline in kidney function varies considerably between patients and the determinants of diabetic nephropathy have not been clearly identified. Therefore, it is very difficult to predict the onset of diabetic nephropathy accurately with simple statistical approaches such as t-test or χ 2-test. To accurately predict the onset of diabetic nephropathy, we applied various machine learning techniques to irregular and unbalanced diabetes dataset, such as support vector machine (SVM) classification and feature selection methods. Visualization of the risk factors was another important objective to give physicians intuitive information on each patient's clinical pattern. Methods and materials We collected medical data from 292 patients with diabetes and performed preprocessing to extract 184 features from the irregular data. To predict the onset of diabetic nephropathy, we compared several classification methods such as logistic regression, SVM, and SVM with a cost sensitive learning method. We also applied several feature selection methods to remove redundant features and improve the classification performance. For risk factor analysis with SVM classifiers, we have developed a new visualization system which uses a nomogram approach. Results Linear SVM classifiers combined with wrapper or embedded feature selection methods showed the best results. Among the 184 features, the classifiers selected the same 39 features and gave 0. 969 of the area under the curve by receiver operating characteristics analysis. The visualization tool was able to present the effect of each feature on the decision via graphical output. Conclusions Our proposed method can predict the onset of diabetic nephropathy about 2–3 months before the actual diagnosis with high prediction performance from an irregular and unbalanced dataset, which statistical methods such as t-test and logistic regression could not achieve. Additionally, the visualization system provides physicians with intuitive information for risk factor analysis. Therefore, physicians can benefit from the automatic early warning of each patient and visualize risk factors, which facilitate planning of effective and proper treatment strategies.

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IJCAI Conference 2003 Conference Paper

SVMC: Single-Class Classification With Support Vector Machines

Hwanjo Yu

Single-Class Classification (SCC) seeks to distinguish one class of data from the universal set of multiple classes. We present a new SCC algorithm that efficiently computes an accurate boundary of the target class from positive and unlabeled data (without labeled negative data).

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