Publications by Topic

Deep Learning on Graphs (Graph Neural Networks and Graph Transformers)

  1. Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
    Jeongwhan Choi*, Jongwoo Kim*, Woosung Kang, Noseong Park
    International Conference on Learning Representations (ICLR), 2026.
    Acceptance Rate 27.4% (5,355/19,525)
    [Paper][arXiv][Code][BibTeX]

  2. Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs
    Jeongwhan Choi, Seungjun Park, Sumin Park, Sung-Bae Cho, Noseong Park
    AAAI Conference on Artificial Intelligence (AAAI), 2026.
    Oral Presentation. Acceptance Rate 17.6% (4,167/23,680)
    [Paper][arXiv][Code][Slides][Poster][BibTeX]

  3. Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
    Jeongwhan Choi, Woosung Kang, Minseo Kim, Jongwoo Kim, Noseong Park
    arXiv preprint, 2025.
    [arXiv][BibTeX]

  4. Predicting Drug-Drug Interactions: A Deep Learning Approach with GCN-Based Collaborative Filtering
    Yeon Uk Jeong, Jeongwhan Choi, Noseong Park, Jae Yong Ryu, Yi Rang Kim
    Artificial Intelligence in Medicine, 2025.
    IF 7.5 (JCR 2022) at submission, IF 6.1 (JCR 2023) at publication
    [Paper][Article (HitNews)][Article (Hankyung)][BibTeX]

  5. PIORF: Physics-Informed Ollivier-Ricci Flow for Long–Range Interactions in Mesh Graph Neural Networks
    Youn-Yeol Yu*, Jeongwhan Choi*, Jaehyeon Park, Kookjin Lee, Noseong Park
    International Conference on Learning Representations (ICLR), 2025.
    Acceptance Rate 32.08%
    [Paper][arXiv][Code][BibTeX]

  6. PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
    Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, Noseong Park
    International Conference on Machine Learning (ICML), 2024.
    Acceptance Rate 27.5%
    [Paper][arXiv][Code][BibTeX]

  7. GREAD: Graph Neural Reaction-Diffusion Networks
    Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
    International Conference on Machine Learning (ICML), 2023.
    Acceptance Rate 27.94% (1,827/6,538)
    [Paper][arXiv][Code][BibTeX]

Transformers

  1. Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain
    Hyowon Wi, Jeongwhan Choi, Noseong Park
    International Joint Conference on Artificial Intelligence (IJCAI), 2025.
    Acceptance Rate 19.3% (1042/5404)
    [Paper][arXiv][Code][BibTeX]

  2. Graph Convolutions Enrich the Self-Attention in Transformers!
    Jeongwhan Choi*, Hyowon Wi*, Jayoung Kim, Yehjin Shin, Kookjin Lee, Nathaniel Trask, Noseong Park
    Conference on Neural Information Processing Systems (NeurIPS), 2024.
    Acceptance Rate 25.8%
    [Paper][arXiv][Code][BibTeX]

  3. Polynomial-based Self-Attention for Table Representation Learning
    Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park
    International Conference on Machine Learning (ICML), 2024.
    Acceptance Rate 27.5%
    [Paper][arXiv][Code][BibTeX]

  4. Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
    Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee, Nayong Kim, Kiseok Chang, Chang-Seung Woo, Ilho Kim, Seok-Woo Lee, Joon-Young Yang, Sooyoung Yoon, Noseong Park
    International Conference on Learning Representations (ICLR), 2024.
    Acceptance Rate 30.81% (2,250/7,304)
    [Paper][arXiv][Code][BibTeX]

  5. An Attentive Inductive Bias for Sequential Recommendation Beyond the Self-Attention
    Yehjin Shin*, Jeongwhan Choi*, Hyowon Wi, Noseong Park
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
    Acceptance Rate 23.75%
    [Paper][arXiv][Code][Patent][BibTeX]

Time-series/Spatiotemporal Forecasting

  1. Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
    Seonkyu Lim, Jaehyeon Park, Seojin Kim, Hyowon Wi, Haksoo Lim, Jinsung Jeon, Jeongwhan Choi, Noseong Park
    IEEE International Conference on Big Data (Big Data), 2023.
    Acceptance Rate 17.49% (92/526)
    [Paper][arXiv][BibTeX]

  2. HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts
    Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
    NeurIPS 2023 Workshop on Distribution Shifts (DistShift), 2023.
    [Paper][BibTeX]

  3. Graph Neural Rough Differential Equations for Traffic Forecasting
    Jeongwhan Choi, Noseong Park
    ACM Transactions on Intelligent Systems and Technology (TIST), 2023.
    IF 10.489 (JCR 2021) at submission, IF 5.0 (JCR 2022) at publication
    [Paper][arXiv][Code][BibTeX]

  4. Climate Modeling with Neural Advection-Diffusion Equation
    Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park
    Knowledge and Information Systems, 2023.
    IF 2.822 (JCR 2020) at submission, IF 2.531 (JCR 2021) at publication
    [Paper][Code][BibTeX]

  5. Graph Neural Controlled Differential Equations for Traffic Forecasting
    Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park
    AAAI Conference on Artificial Intelligence (AAAI), 2022.
    Oral Presentation (Acceptance rate: 5.5%). Overall Acceptance rate: 15.2% (1,370/9,020).
    [Paper][arXiv][Code][Poster][Patent][BibTeX]

Recommender Systems

  1. Knowledge World Model: Latent Rollouts for Learning Path Recommendation
    Jongwoo Kim, Seongyeub Chu, Jeongwhan Choi, Jiwon Jeong and Mun Yong Yi
    ACM International Conference on Information and Knowledge Management (CIKM), 2026.
    Acceptance Rate 27% (597/2,216)
    [BibTeX]

  2. One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets
    Woosung Kang, Jiwon Jeong, Jonghyeok Shin, Jeongwhan Choi, Noseong Park
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026.
    Oral Presentation. Acceptance Rate 18.5%
    [Paper][arXiv][Code][BibTeX]

  3. TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
    Yehjin Shin, Jeongwhan Choi, Seojin Kim, Noseong Park
    Conference on Neural Information Processing Systems (NeurIPS), 2025.
    Acceptance Rate 25.52% (5,290/21,575)
    [Paper][arXiv][Code][BibTeX]

  4. SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation
    Chaejeong Lee*, Jeongwhan Choi*, Hyowon Wi, Sung-Bae Cho, Noseong Park
    The 18th ACM International Conference on Web Search and Data Mining (WSDM), 2025.
    Acceptance Rate 17.3%
    [Paper][arXiv][Code][Poster][BibTeX]

  5. Graph Signal Processing for Cross-Domain Recommendation
    Jeongeun Lee, Seongku Kang, Won-Yong Shin, Jeongwhan Choi, Noseong Park, Dongha Lee
    arXiv preprint, 2024.
    [arXiv][BibTeX]

  6. SVD-AE: Simple Autoencoders for Collaborative Filtering
    Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, Noseong Park
    International Joint Conference on Artificial Intelligence (IJCAI), 2024.
    Acceptance Rate 14.00%
    [Paper][arXiv][Code][BibTeX]

  7. An Attentive Inductive Bias for Sequential Recommendation Beyond the Self-Attention
    Yehjin Shin*, Jeongwhan Choi*, Hyowon Wi, Noseong Park
    AAAI Conference on Artificial Intelligence (AAAI), 2024.
    Acceptance Rate 23.75%
    [Paper][arXiv][Code][Patent][BibTeX]

  8. RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
    Jeongwhan Choi*, Hyowon Wi*, Chaejeong Lee, Sung-Bae Cho, Dongha Lee, Noseong Park
    arXiv preprint, 2023.
    [arXiv][BibTeX]

  9. Blurring-Sharpening Process Models for Collaborative Filtering
    Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
    Proceedings of the ACM Conference on Research and Development in Information Retrieval (SIGIR), 2023.
    Acceptance Rate 20.1% (165/822)
    [Paper][arXiv][Code][BibTeX]

  10. Linear, or Non-Linear, That is the Question!
    Taeyong Kong, Taeri Kim, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, Sang-Wook Kim
    Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM), 2022.
    Regular Paper Acceptance Rate 15.8%. Overall Acceptance Rate 18%.
    [Paper][arXiv][Code][BibTeX]

  11. LT-OCF: Learnable-Time ODE-based Collaborative Filtering
    Jeongwhan Choi, Jinsung Jeon, Noseong Park
    Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.
    Regular Paper Acceptance Rate 21.7%. Overall Acceptance Rate 22%.
    [Paper][arXiv][Code][Patent][BibTeX]

AI4Science

  1. How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators
    Jihyeon Hur, Yongseok Kwon, Min-Gi Jo, Jeongwhan Choi, Noseong Park
    AI4Physics Workshop at ICML, 2026.
    [Paper][arXiv][BibTeX]

  2. Predicting Drug-Drug Interactions: A Deep Learning Approach with GCN-Based Collaborative Filtering
    Yeon Uk Jeong, Jeongwhan Choi, Noseong Park, Jae Yong Ryu, Yi Rang Kim
    Artificial Intelligence in Medicine, 2025.
    IF 7.5 (JCR 2022) at submission, IF 6.1 (JCR 2023) at publication
    [Paper][Article (HitNews)][Article (Hankyung)][BibTeX]

  3. PIORF: Physics-Informed Ollivier-Ricci Flow for Long–Range Interactions in Mesh Graph Neural Networks
    Youn-Yeol Yu*, Jeongwhan Choi*, Jaehyeon Park, Kookjin Lee, Noseong Park
    International Conference on Learning Representations (ICLR), 2025.
    Acceptance Rate 32.08%
    [Paper][arXiv][Code][BibTeX]

  4. Graph-Based Representation Approach for Deep Learning of Organic Light-Emitting Diode Devices
    Taeyang Lee, Jeongwhan Choi, Inyeob Na, Insun Yoo, Sungil Woo, Kwang Jong Kim, Mikyung Park, Joonghwan Yang, Jeongguk Min, Seokwoo Lee, Noseong Park, Joonyoung Yang
    Advanced Intelligent Systems, 2024.
    IF 6.8 (JCR 2023) at submission and publication
    [Paper][BibTeX]

  5. Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
    Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee, Nayong Kim, Kiseok Chang, Chang-Seung Woo, Ilho Kim, Seok-Woo Lee, Joon-Young Yang, Sooyoung Yoon, Noseong Park
    International Conference on Learning Representations (ICLR), 2024.
    Acceptance Rate 30.81% (2,250/7,304)
    [Paper][arXiv][Code][BibTeX]

  6. Climate Modeling with Neural Advection-Diffusion Equation
    Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park
    Knowledge and Information Systems, 2023.
    IF 2.822 (JCR 2020) at submission, IF 2.531 (JCR 2021) at publication
    [Paper][Code][BibTeX]

  7. Climate Modeling with Neural Diffusion Equations
    Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, Noseong Park
    IEEE International Conference on Data Mining (ICDM), 2021.
    Regular Paper Acceptance Rate 9.9%. Overall Acceptance Rate 20%.
    [Paper][arXiv][Code][BibTeX]

AI4Finance

  1. FraudCenGCL: Role-Aware Graph Contrastive Learning for Low-Homophily Fraud Detection
    Seonkyu Lim*, Jeongwhan Choi*, Jaehoon Lee*
    IEEE International Conference on Big Data (Big Data), 2025.
    Industrial & Government Track
    [Paper][BibTeX]

  2. FraudCenGCL: Enhancing Fraud Detection via Dual-View Graph Contrastive Learning with Account Centrality Features
    Seonkyu Lim*, Jeongwhan Choi*, Noseong Park
    AI for Social Impact: Bridging Innovations in Finance, Social Media, and Crime Prevention Workshop at AAAI, 2025.
    [Paper][Workshop][Talk][BibTeX]

  3. FrAug: Enhanced Fraud Detection in Interbank Transfers via Augmented Account Features
    Seonkyu Lim*, Jeongwhan Choi*, Jaehoon Lee, Noseong Park
    AI4TS Workshop at AAAI, 2025.
    [Paper][BibTeX]

  4. FrAug: Enhanced Fraud Detection in Interbank Transfers via Augmented Account Features
    Seonkyu Lim*, Jeongwhan Choi*, Jaehoon Lee, Noseong Park
    IEEE International Conference on Big Data and Smart Computing (BigComp), 2025.
    [Paper][BibTeX]

  5. Enhanced Fraud Detection in Bank Transfers via Augmented Account Features
    Seonkyu Lim*, Jeongwhan Choi*, Jaehoon Lee, Noseong Park
    ACM ICAIF Workshop on Foundation Models for Time Series (FM4TS), 2024.
    Accepted for oral presentation
    [Paper][Workshop][etnews][ZDNET KOREA][BibTeX]

  6. Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
    Seonkyu Lim*, Jeongwhan Choi*, Noseong Park, Sang-Ha Yoon, Shinhyuck Kang, Young-Min Kim, Hyunjoong Kang
    Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024.
    Applied Research Paper Acceptance Rate 32.59%
    [Paper][arXiv][Code][BibTeX]

Software Engineering

  1. QoS-Aware Graph Contrastive Learning for Web Service Recommendation
    Jeongwhan Choi, Duksan Ryu
    Asia-Pacific Software Engineering Conference (APSEC), 2023.
    Acceptance Rate 33.5%
    [Paper][arXiv][BibTeX]

  2. Graph Convolution-based Collaborative Filtering for Web Service QoS Ranking
    Jeongwhan Choi, Duksan Ryu
    Proceedings of the 25th Korea Conference on Software Engineering (KCSE), 2023. pp. 58–67.
    [Paper][BibTeX]

  3. Self-Supervised Learning Using Feature Subsets of Software Defect Data
    Jeongwhan Choi, Duksan Ryu
    Korea Software Congress (KSC), 2021.
    [Paper][BibTeX]

  4. Bayesian Optimization Framework for Improved Cross-Version Defect Prediction
    Jeongwhan Choi, Duksan Ryu
    KIPS Transactions on Software and Data Engineering (KTSDE), 2021.
    [Paper][BibTeX]

  5. Bayesian Optimization Framework for Cross-Version Defect Prediction
    Jeongwhan Choi, Duksan Ryu
    Proceedings of the 23rd Korea Conference on Software Engineering (KCSE), 2021.
    Outstanding Paper Award (우수논문상).
    [PDF][Video][BibTeX]

  6. Improved Prediction for Configuration Bug Report Using Text Mining and Dimensionality Reduction
    Jeongwhan Choi, Jiwon Choi, Duksan Ryu, Suntae Kim
    Journal of KIISE, 2021.
    [Paper][BibTeX]

  7. A Study on the Applicability of Transfer Learning Techniques for Cross-Project Defect Regression
    Jeongwhan Choi, Duksan Ryu
    Korea Software Congress (KSC), 2020.
    [Paper][BibTeX]

  8. Comparative Study of Transfer Learning Models for Cross-Project Automotive Software Defect Prediction
    Jeongwhan Choi, Duksan Ryu, Suntae Kim
    Korea Computer Congress (KCC), 2020.
    [Paper][BibTeX]

  9. Prediction for Configuration Bug Report Using Text Mining
    Jeongwhan Choi, Jiwon Choi, Duksan Ryu, Suntae Kim
    Proceedings of the 22nd Korea Conference on Software Engineering (KCSE), 2020.
    [Paper][BibTeX]

  10. A Software Module That Analyzes the Relationship Between Headline and Content of the Web Article: CHIMERA
    Seounghan Song, Jeongwhan Choi, Mingu Kang, Cheoljung Yoo
    Proceedings of KIIT Conference, 2019.
    [Paper][BibTeX]

Misc.

  1. Learning Hierarchical Forward Processes For Discrete Diffusion Language Models
    Inhyeok Jeong, Jeongwhan Choi, Jaehyeon Park, Noseong Park
    Conference on Neural Information Processing Systems (NeurIPS), 2026.
    [BibTeX]

  2. Possibility for Proactive Anomaly Detection
    Jinsung Jeon, Jaehyeon Park, Sewon Park, Jeongwhan Choi, Minjung Kim, Noseong Park
    ICBINB Workshop at ICLR, 2025.
    [Paper][Patent][BibTeX]

  3. Prediction-based One-shot Dynamic Parking Pricing
    Seoyoung Hong, Heejoo Shin, Jeongwhan Choi, Noseong Park
    Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022.
    Regular Paper Acceptance Rate 23.23%
    [Paper][arXiv][Code][BibTeX]

  4. Prediction Techniques for Difficulty Level of Hanja Using Multiple Linear Regression
    Jeongwhan Choi, Jiwoo Noh, Suntae Kim
    J. Inst. Internet, Broadcast. Commun., 2019.
    [Paper][Patent][BibTeX]

  5. Iceberg-Ship Classification in SAR Images Using Convolutional Neural Network with Transfer Learning
    Jeongwhan Choi
    Journal of Internet Computing & Services, vol. 19, no. 4, pp. 35–44, 2018.
    [Paper][BibTeX]