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A curated list of publications from BigDyL
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A curated list of publications from BigDyL
- Host: GitHub
- URL: https://github.com/bigdyl-kaist/awesome-bigdyl
- Owner: bigdyl-kaist
- Created: 2024-08-29T14:23:56.000Z (21 days ago)
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Metadata Files:
- Readme: README.md
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README
# Awesome BigDyL
A curated list of publications from the Laboratory of Big Data Analytics and Learning (BigDyL) at KAIST (formerly at Yonsei University).
> BigDyL was established in March 2020 at Yonsei University and moved to KAIST in February 2024. For more information, visit [our website](https://sites.google.com/view/npark).
## Table of Contents
- [Papers by Year](#papers-by-year)
- [2024](#2024)
- [2023](#2023)
- [2022](#2022)
- [2021](#2021)
- [Papers by Topic](#papers-by-topic)
## Papers by Year
### 2024
- Seonkyu Lim, Jeongwhan Choi, Noseong Park, Sang-Ha Yoon, Shinhyuck Kang, Young-Min Kim, and Hyunjoong Kang, "Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP," ACM International Conference on Information and Knowledge Management (CIKM), 2024.
- Fan Wu, Woojin Cho, David Korotky, Sanghyun Hong, Donsub Rim, Noseong Park, and Kookjin Lee, "Identifying Contemporaneous and Lagged Dependence Structures by Promoting Sparsity in Continuous-time Neural Networks," ACM International Conference on Information and Knowledge Management (CIKM), 2024.
- Sheo Yon Jhin, Seojin Kim, and Noseong Park, "Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024. [[Paper]](https://dl.acm.org/doi/10.1145/3637528.3671969)[[Code]](https://github.com/sheoyon-jhin/contime) BibTeX
@inproceedings{jhin2024contime,
title={Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization},
author={Jhin, Sheo Yon and Kim, Seojin and Park, Noseong},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={1234--1245},
year={2024}
}
- Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, and Noseong Park, "Polynomial-based Self-Attention for Table Representation Learning," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/kim24ae.html) BibTeX
@InProceedings{kim24tabpsa,
title = {Polynomial-based Self-Attention for Table Representation Learning},
author = {Kim, Jayoung and Shin, Yehjin and Choi, Jeongwhan and Wi, Hyowon and Park, Noseong},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {24509--24526},
year = {2024},
volume = {235},
}
- Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, and Noseong Park, "PANDA: Expanded Width-Aware Message Passing Beyond Rewiring," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/choi24f.html)[[Code]](https://github.com/jeongwhanchoi/panda) BibTeX
@inproceedings{choi2024panda,
title = {{PANDA}: Expanded Width-Aware Message Passing Beyond Rewiring},
author = {Choi, Jeongwhan and Park, Sumin and Wi, Hyowon and Cho, Sung-Bae and Park, Noseong},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {8740--8761},
year = {2024},
volume = {235},
publisher = {PMLR},
}
- Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park, "Parameterized Physics-informed Neural Networks for Parameterized PDEs," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/cho24b.html)[[Code]](https://github.com/WooJin-Cho/Parameterized-Physics-informed-Neural-Networks) BibTeX
@inproceedings{cho2024p2inn,
title = {Parameterized Physics-informed Neural Networks for Parameterized {PDE}s},
author = {Cho, Woojin and Jo, Minju and Lim, Haksoo and Lee, Kookjin and Lee, Dongeun and Hong, Sanghyun and Park, Noseong},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {8510--8533},
year = {2024},
volume = {235},
publisher = {PMLR},
}
- Hyeyoon Lee, Kanghyun Choi, Dain Kwon, SunJong Park, Mayoore Selvarasa Jaiswal, Noseong Park, Jonghyun Choi, and Jinho Lee, "DataFreeShield: Defending Adversarial Attacks without Training Data," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/lee24f.html)[[Code]](https://github.com/hylee817/datafreeshield) BibTeX
@inproceedings{lee2024datafreeshield,
title={DataFreeShield: Defending Adversarial Attacks without Training Data},
author={Lee, Hyeyoon and Choi, Kanghyun and Kwon, Dain and Park, Sunjong and Jaiswal, Mayoore Selvarasa and Park, Noseong and Choi, Jonghyun and Lee, Jinho},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2024}
}
- Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, and Noseong Park, "SVD-AE: Simple Autoencoders for Collaborative Filtering," International Joint Conference on Artificial Intelligence (IJCAI), 2024. [[Paper]](https://www.ijcai.org/proceedings/2024/227)[[Code]](https://github.com/seoyoungh/svd-ae) BibTeX
@inproceedings{hong2024svdae,
title = {SVD-AE: Simple Autoencoders for Collaborative Filtering},
author = {Hong, Seoyoung and Choi, Jeongwhan and Lee, Yeon-Chang and Kumar, Srijan and Park, Noseong},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {2054--2062},
year = {2024},
doi = {10.24963/ijcai.2024/227},
url = {https://doi.org/10.24963/ijcai.2024/227},
}
- Jinsung Jeon and Noseong Park, "SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations," ICLR Workshop on Practical ML for Limited/Low-resource Settings (PML4LRS), 2024. [[Paper]](https://openreview.net/forum?id=01sjjq6m4F)BibTeX
@inproceedings{jeon2024spigan,
title={{SPI}-{GAN}: Denoising Diffusion {GAN}s with Straight-Path Interpolations},
author={Jinsung Jeon and Noseong Park},
booktitle={5th Workshop on practical ML for limited/low resource settings},
year={2024},
url={https://openreview.net/forum?id=01sjjq6m4F}
}
- Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park, "Extension of Physics-informed Neural Networks to Solving Parameterized PDEs," ICLR Workshop on AI4DifferentialEquations in Science (AI4DiffEqtnsInSci), 2024. [[Paper]](https://openreview.net/forum?id=EAkRlHFLBc&referrer=%5Bthe%20profile%20of%20Noseong%20Park%5D(%2Fprofile%3Fid%3D~Noseong_Park1))BibTeX
@inproceedings{cho2024extension,
title={Extension of Physics-informed Neural Networks to Solving Parameterized {PDE}s},
author={Woojin Cho and Minju Jo and Haksoo Lim and Kookjin Lee and Dongeun Lee and Sanghyun Hong and Noseong Park},
booktitle={ICLR 2024 Workshop on AI4DifferentialEquations In Science},
year={2024},
url={https://openreview.net/forum?id=EAkRlHFLBc}
}
- Jinsung Jeon, Hyundong Jin, Jonghyun Choi, Sanghyun Hong, Dongeun Lee, Kookjin Lee, and Noseong Park, "PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images," International Conference on Learning Representations (ICLR), 2024. [[Paper]](https://openreview.net/forum?id=Cf4FJGmHRQ) BibTeX
@inproceedings{jeon2024pacfno,
title={{PAC}-{FNO}: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images},
author={Jinsung Jeon and Hyundong Jin and Jonghyun Choi and Sanghyun Hong and Dongeun Lee and Kookjin Lee and Noseong Park},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Cf4FJGmHRQ}
}
- Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee, Nayong Kim, Kiseok Chang, Changseung Woo, Ilho Kim, Seokwoo Lee, Joon Young Yang, Sooyoung Yoon, and Noseong Park, "Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer," International Conference on Learning Representations (ICLR), 2024. [[Paper]](https://openreview.net/forum?id=90yw2uM6J5)[[Code]](https://github.com/yuyudeep/hcmt) BibTeX
@inproceedings{
yu2024learning,
title={Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer},
author={Youn-Yeol Yu and Jeongwhan Choi and Woojin Cho and Kookjin Lee and Nayong Kim and Kiseok Chang and ChangSeung Woo and Ilho Kim and SeokWoo Lee and Joon Young Yang and Sooyoung Yoon and Noseong Park},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=90yw2uM6J5}
}
- Yehjin Shin, Jeongwhan Choi, Hyowon Wi, and Noseong Park, "An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention," AAAI Conference on Artificial Intelligence (AAAI), 2024. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/28747)[[Code]](https://github.com/yehjin-shin/BSARec) BibTeX
@inproceedings{shin2024bsarec,
title={An attentive inductive bias for sequential recommendation beyond the self-attention},
author={Shin, Yehjin and Choi, Jeongwhan and Wi, Hyowon and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={8},
pages={8984--8992},
year={2024}
}
- Woojin Cho, Seunghyeon Cho, Hyundong Jin, Jinsung Jeon, Kookjin Lee, Sanghyun Hong, Dongeun Lee, Jonghyun Choi, and Noseong Park, "Operator-learning-inspired Modeling of Neural Ordinary Differential Equations," AAAI Conference on Artificial Intelligence (AAAI), 2024. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29036)[[Code]](https://github.com/WooJin-Cho/BFNO-NODE) BibTeX
@inproceedings{cho2024bfno,
title={Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations},
author={Cho, Woojin and Cho, Seunghyeon and Jin, Hyundong and Jeon, Jinsung and Lee, Kookjin and Hong, Sanghyun and Lee, Dongeun and Choi, Jonghyun and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={10},
pages={11543--11551},
year={2024}
}
- Hyowon Wi, Yehjin Shin, and Noseong Park, "Continuous-time Autoencoders for Regular and Irregular Time Series Imputation," ACM International Web Search and Data Mining Conference (WSDM), 2024. [[Paper]](https://dl.acm.org/doi/10.1145/3616855.3635831) BibTeX
@inproceedings{wi2024cta
author = {Wi, Hyowon and Shin, Yehjin and Park, Noseong},
title = {Continuous-time Autoencoders for Regular and Irregular Time Series Imputation},
year = {2024},
publisher = {Association for Computing Machinery},
doi = {10.1145/3616855.3635831},
booktitle = {Proceedings of the 17th ACM International Conference on Web Search and Data Mining},
pages = {826–835},
numpages = {10},
}
- Sheo Yon Jhin, Heejoo Shin, Sujie Kim, Seoyoung Hong, Solhee Park, Noseong Park, Seungbeom Lee, Hwiyoung Maeng, and Seungmin Jeon, "Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting," Knowledge and Information Systems (IF=3.205). [[Paper]](https://link.springer.com/article/10.1007/s10115-023-01977-5)[[Code]](https://github.com/sheoyon-jhin/ANCDE) BibTeX
@article{jhin2024attentive,
title={Attentive neural controlled differential equations for time-series classification and forecasting},
author={Jhin, Sheo Yon and Shin, Heejoo and Kim, Sujie and Hong, Seoyoung and Jo, Minju and Park, Solhee and Park, Noseong and Lee, Seungbeom and Maeng, Hwiyoung and Jeon, Seungmin},
journal={Knowledge and Information Systems},
volume={66},
number={3},
pages={1885--1915},
year={2024},
publisher={Springer}
}
### 2023
- Seonkyu Lim, Jaehyeon Park, Seojin Kim, Hyowon Wi, Haksoo Lim, Jinsung Jeon, Jeongwhan Choi, and Noseong Park, "Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations," IEEE International Conference on Big Data (IEEE BigData), 2023. [[Paper]](https://www.computer.org/csdl/proceedings-article/bigdata/2023/10386388/1TUP4OY1IT6) BibTeX
@inproceedings{lim2023dnode,
title={Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations},
author={Lim, Seonkyu and Park, Jaehyeon and Kim, Seojin and Wi, Hyowon and Lim, Haksoo and Jeon, Jinsung and Choi, Jeongwhan and Park, Noseong},
booktitle={2023 IEEE International Conference on Big Data (BigData)},
pages={748--757},
year={2023},
organization={IEEE}
}
- Woojin Cho, Kookjin Lee, Donsub Rim, and Noseong Park, "Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks," Conference on Neural Information Processing Systems (NeurIPS), 2023. [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/24f8dd1b8f154f1ee0d7a59e368eccf3-Abstract-Conference.html)[[Code]](https://github.com/WooJin-Cho/Hyper-LR-PINN) BibTeX
@inproceedings{cho2023hyper,
author = {Cho, Woojin and Lee, Kookjin and Rim, Donsub and Park, Noseong},
title = {Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks},
booktitle = {Advances in Neural Information Processing Systems},
pages = {11219--11231},
volume = {36},
year = {2023}
}
- Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park, "HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts," NeurIPS Workshop on Distribution Shifts (DistShift), 2023. [[Paper]](https://openreview.net/forum?id=7yVBYSPI8Z)BibTeX
@inproceedings{lee2024hypergpa,
title={HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts},
author={Jaehoon Lee and Chan Kim and Gyumin Lee and Haksoo Lim and Jeongwhan Choi and Kookjin Lee and Dongeun Lee and Sanghyun Hong and Noseong Park},
booktitle={NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models},
year={2024},
url={https://openreview.net/forum?id=7yVBYSPI8Z}
}
- Haksoo Lim, Sewon Park, Minjung Kim, Jaehoon Lee, Seonkyu Lim, and Noseong Park, "MadSGM: Multivariate Anomaly Detection with Score-based Generative Models," ACM International Conference on Information and Knowledge Management (CIKM), 2023. [[Paper]](https://dl.acm.org/doi/10.1145/3583780.3614956) BibTeX
@inproceedings{lim2023madsgm,
title={Madsgm: Multivariate anomaly detection with score-based generative models},
author={Lim, Haksoo and Park, Sewon and Kim, Minjung and Lee, Jaehoon and Lim, Seonkyu and Park, Noseong},
booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages={1411--1420},
year={2023}
}
- Sheo Yon Jhin, Jaehoon Lee, and Noseong Park, "Precursor-of-Anomaly Detection for Irregular Time Series," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3580305.3599469)[[Code]](https://github.com/sheoyon-jhin/pad) BibTeX
@inproceedings{jhin2023precursor,
title={Precursor-of-anomaly detection for irregular time series},
author={Jhin, Sheo Yon and Lee, Jaehoon and Park, Noseong},
booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={917--929},
year={2023}
}
- JaeYeon Park, Kichang Lee, Noseong Park, Seng Chan You, and JeongGil Ko, "Self-Attention LSTM-FCN Model for Arrhythmia Classification and Uncertainty Assessment," to appear in Artificial Intelligence In Medicine (IF=7.011), 2023. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0933365723000842) BibTeX
@article{park2023self,
title={Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment},
author={Park, JaeYeon and Lee, Kichang and Park, Noseong and You, Seng Chan and Ko, JeongGil},
journal={Artificial Intelligence in Medicine},
volume={142},
pages={102570},
year={2023},
publisher={Elsevier}
}
- Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho, "GREAD: Graph Neural Reaction-Diffusion Networks," International Conference on Machine Learning (ICML), 2023. [[Paper]](https://proceedings.mlr.press/v202/choi23a.html)[[Code]](https://github.com/jeongwhanchoi/gread) BibTeX
@inproceedings{choi2023gread,
title={{GREAD}: Graph neural reaction-diffusion networks},
author={Choi, Jeongwhan and Hong, Seoyoung and Park, Noseong and Cho, Sung-Bae},
booktitle={International Conference on Machine Learning},
pages={5722--5747},
year={2023},
organization={PMLR}
}
- Chaejeong Lee, Jayoung Kim, and Noseong Park, "CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis," International Conference on Machine Learning (ICML), 2023. [[Paper]](https://proceedings.mlr.press/v202/lee23i.html)[[Code]](https://github.com/chaejeonglee/codi) BibTeX
@inproceedings{lee2023codi,
title={Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis},
author={Lee, Chaejeong and Kim, Jayoung and Park, Noseong},
booktitle={International Conference on Machine Learning},
pages={18940--18956},
year={2023},
organization={PMLR}
}
- Minju Jo, Seungji Kook, and Noseong Park, "Hawkes Process based on Controlled Differential Equations," International Joint Conference on Artificial Intelligence (IJCAI), 2023. [[Paper]](https://www.ijcai.org/proceedings/2023/239)[[Code]](https://github.com/kookseungji/Hawkes-Process-Based-on-Controlled-Differential-Equations) BibTeX
@inproceedings{jo2023hawkes,
title={Hawkes process based on controlled differential equations},
author={Jo, Minju and Kook, Seungji and Park, Noseong},
booktitle={Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence},
pages={2151--2159},
year={2023}
}
- Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho, "Blurring-Sharpening Process Models for Collaborative Filtering," International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3539618.3591645)[[Code]](https://github.com/jeongwhanchoi/BSPM) BibTeX
@inproceedings{choi2023bspm,
title={Blurring-Sharpening Process Models for Collaborative Filtering},
author={Choi, Jeongwhan and Hong, Seoyoung and Park, Noseong and Cho, Sung-Bae},
booktitle={Proceedings of the ACM Conference on Research and Development in Information Retrieval (SIGIR)},
year={2023}
}
- Jeongwhan Choi, and Noseong Park, "Graph Neural Rough Differential Equations for Traffic Forecasting," ACM Transactions on Intelligent Systems and Technology (IF=10.489), Vol. 14, No. 4, 2023. [[Paper]](https://dl.acm.org/doi/10.1145/3604808)[[Code]](https://github.com/jeongwhanchoi/STG-NRDE) BibTeX
@article{choi2023graph,
title={Graph neural rough differential equations for traffic forecasting},
author={Choi, Jeongwhan and Park, Noseong},
journal={ACM Transactions on Intelligent Systems and Technology},
volume={14},
number={4},
pages={1--27},
year={2023},
publisher={ACM New York, NY}
}
- Jaewon Jung, Jaeyong Song, Hongsun Jang, Hyeyoon Lee, Kanghyun Choi, Noseong Park, and Jinho Lee, "Fast Adversarial Training with Dynamic Batch-level Attack Control," Design Automation Conference (DAC), 2023. [[Paper]](https://ieeexplore.ieee.org/document/10247930) BibTeX
@inproceedings{jung2023fast,
title={Fast Adversarial Training with Dynamic Batch-level Attack Control},
author={Jung, Jaewon and Song, Jaeyong and Jang, Hongsun and Lee, Hyeyoon and Choi, Kanghyun and Park, Noseong and Lee, Jinho},
booktitle={2023 60th ACM/IEEE Design Automation Conference (DAC)},
pages={1--6},
year={2023},
organization={IEEE}
}
- Jayoung Kim, Chaejeong Lee, and Noseong Park, "STaSy: Score-based Tabular Data Synthesis," International Conference on Learning Representations (ICLR), 2023. [[Paper]](https://openreview.net/forum?id=1mNssCWt_v)[[Code]](https://github.com/JayoungKim408/STaSy) BibTeX
@inproceedings{kim2024stasy,
title={STaSy: Score-based Tabular data Synthesis},
author={Kim, Jayoung and Lee, Chaejeong and Park, Noseong},
year={2023},
booktitle={The Eleventh International Conference on Learning Representations}
}
- Sheo Yon Jhin, Minju Jo, Seungji Kook, and Noseong Park, "Learnable Path in Neural Controlled Differential Equations," AAAI Conference on Artificial Intelligence (AAAI), 2023. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29036)BibTeX
@inproceedings{jhin2023leap,
title={Learnable path in neural controlled differential equations},
author={Jhin, Sheo Yon and Jo, Minju and Kook, Seungji and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={7},
pages={8014--8022},
year={2023}
}
### 2022
- Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, and Noseong Park, "TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering," IEEE International Conference on Big Data (IEEE BigData), 2022 [Best Student Paper Award].[[Paper]](https://ieeexplore.ieee.org/abstract/document/10020773) BibTeX
@inproceedings{hong2022timekit,
title={{TimeKit}: A time-series forecasting-based upgrade kit for collaborative filtering},
author={Hong, Seoyoung and Jo, Minju and Kook, Seungji and Jung, Jaeeun and Wi, Hyowon and Park, Noseong and Cho, Sung-Bae},
booktitle={2022 IEEE International Conference on Big Data (Big Data)},
pages={565--574},
year={2022},
organization={IEEE}
}
- Jinsung Jeon, Jeonghak Kim, Haryong Song, Seunghyeon Cho, and Noseong Park, "GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks," Conference on Neural Information Processing Systems (NeurIPS), 2022. [[Paper]](https://openreview.net/forum?id=ez6VHWvuXEx)[[Code]](https://github.com/Jinsung-Jeon/GT-GAN) BibTeX
@article{jeon2022gt,
title={GT-GAN: General purpose time series synthesis with generative adversarial networks},
author={Jeon, Jinsung and Kim, Jeonghak and Song, Haryong and Cho, Seunghyeon and Park, Noseong},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={36999--37010},
year={2022}
}
- Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, and Noseong Park, "Climate Modeling with Neural Advection-Diffusion Equations," Knowledge and Information Systems (IF=3.205). [[Paper]](https://link.springer.com/article/10.1007/s10115-023-01829-2)[[Code]](https://github.com/hwangyong753/NADE) BibTeX
@article{choi2023climate,
title={Climate modeling with neural advection--diffusion equation},
author={Choi, Hwangyong and Choi, Jeongwhan and Hwang, Jeehyun and Lee, Kookjin and Lee, Dongeun and Park, Noseong},
journal={Knowledge and Information Systems},
volume={65},
number={6},
pages={2403--2427},
year={2023},
publisher={Springer}
}
- Taeri Kim, Noseong Park, Jiwon Hong, and Sang-Wook Kim, "Phishing URL Detection: A Network-based Approach Robust to Evasion," ACM Conference on Computer and Communications Security (CCS), 2022. [[Paper]](https://dl.acm.org/doi/10.1145/3548606.3560615)[[Code]](https://github.com/taerikkk/bpe) BibTeX
@inproceedings{kim2022phishing,
title={Phishing url detection: A network-based approach robust to evasion},
author={Kim, Taeri and Park, Noseong and Hong, Jiwon and Kim, Sang-Wook},
booktitle={Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security},
pages={1769--1782},
year={2022}
}
- Seoyoung Hong, Heejoo Shin, Jeongwhan Choi, and Noseong Park, "Prediction-based One-shot Dynamic Parking Pricing," ACM International Conference on Information and Knowledge Management (CIKM), 2022, Full Research Paper. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3511808.3557421)[[Code]](https://github.com/seoyoungh/one-shot-optimization) BibTeX
@inproceedings{hong2022prediction,
title={Prediction-based one-shot dynamic parking pricing},
author={Hong, Seoyoung and Shin, Heejoo and Choi, Jeongwhan and Park, Noseong},
booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
pages={748--757},
year={2022}
}
- Jihyeon Hyeong, Jayoung Kim, Noseong Park, and Sushil Jajodia, "An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models," ACM International Conference on Information and Knowledge Management (CIKM), 2022, Short Research Paper. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3511808.3557546)[[Code]](https://github.com/jayoungkim408/mia) BibTeX
@inproceedings{hyeong2022empirical,
title={An empirical study on the membership inference attack against tabular data synthesis models},
author={Hyeong, Jihyeon and Kim, Jayoung and Park, Noseong and Jajodia, Sushil},
booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
pages={4064--4068},
year={2022}
}
- Manh Tuan Do, Noseong Park, and Kijung Shin, "Two-Stage Training of Graph Neural Networks for Graph Classification," to appear in Neural Processing Letters (IF=2.565). [[Paper]](https://link.springer.com/article/10.1007/s11063-022-10985-5)[[Code]](https://github.com/manhtuando97/two-stage-gnn) BibTeX
@article{do2023two,
title={Two-stage training of graph neural networks for graph classification},
author={Do, Manh Tuan and Park, Noseong and Shin, Kijung},
journal={Neural Processing Letters},
volume={55},
number={3},
pages={2799--2823},
year={2023},
publisher={Springer}
}
- Seunghyeon Cho, Sanghyun Hong, Kookjin Lee, and Noseong Park, "AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation," ICML Workshop on Continuous Time Methods for Machine Learning, 2022. [[Paper]](https://arxiv.org/abs/2207.06066)[[Code]](https://github.com/Seunghyeon-Cho/AdamNODE) BibTeX
@article{cho2022adamnodes,
title={AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation},
author={Cho, Suneghyeon and Hong, Sanghyun and Lee, Kookjin and Park, Noseong},
journal={arXiv preprint arXiv:2207.06066},
year={2022}
}
- Jayoung Kim, ChaeJeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, and Jihoon Cho, "SOS: Score-based Oversampling Minor Classes for Tabular Data," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3534678.3539454)[[Code]](https://github.com/JayoungKim408/SOS) BibTeX
@inproceedings{kim2022sos,
title={Sos: Score-based oversampling for tabular data},
author={Kim, Jayoung and Lee, Chaejeong and Shin, Yehjin and Park, Sewon and Kim, Minjung and Park, Noseong and Cho, Jihoon},
booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={762--772},
year={2022}
}
- Kanghyun Choi, Hye Yoon Lee, Deokki Hong, Joonsang Yu, Noseong Park, Youngsok Kim, and Jinho Lee, "It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [[Paper]](https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.html)[[Code]](https://github.com/iamkanghyunchoi/ait) BibTeX
@inproceedings{choi2022s,
title={It's all in the teacher: Zero-shot quantization brought closer to the teacher},
author={Choi, Kanghyun and Lee, Hye Yoon and Hong, Deokki and Yu, Joonsang and Park, Noseong and Kim, Youngsok and Lee, Jinho},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8311--8321},
year={2022}
}
- Thai Le, Noseong Park, and Dongwon Lee, "SHIELD: Defending Textual Neural Networks against Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher," Annual Meeting of the Association for Computational Linguistics (ACL), 2022. [[Paper]](https://aclanthology.org/2022.acl-long.459/)[[Code]](https://github.com/lethaiq/shield-defend-adversarial-texts) BibTeX
@inproceedings{le2022shield,
title={SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher},
author={Le, Thai and Park, Noseong and Lee, Dongwon},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={6661--6674},
year={2022}
}
- Deokki Hong, Kanghyun Choi, Hey Yoon Lee, Joonsang Yu, Youngsok Kim, Noseong Park, and Jinho Lee, "Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration," Design Automation Conference (DAC), 2022. [[Paper]](https://dl.acm.org/doi/10.1145/3489517.3530507) BibTeX
@inproceedings{hong2022enabling,
title={Enabling hard constraints in differentiable neural network and accelerator co-exploration},
author={Hong, Deokki and Choi, Kanghyun and Lee, Hye Yoon and Yu, Joonsang and Park, Noseong and Kim, Youngsok and Lee, Jinho},
booktitle={Proceedings of the 59th ACM/IEEE Design Automation Conference},
pages={589--594},
year={2022}
}
- Jaehoon Lee, Jinsung Jeon, Sheo yon Jhin, Jihyeon Hyeong, Jayoung Kim, Minju Jo, Seungji Kook, and Noseong Park, "LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations," International Conference on Learning Representations (ICLR), 2022. [[Paper]](https://openreview.net/forum?id=fCG75wd39ze)[[Code]](https://github.com/leejaehoon2016/LORD) BibTeX
@inproceedings{lee2022lord,
title={{LORD}: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations},
author={LEE, JAEHOON and Jeon, Jinsung and yon Jhin, Sheo and Hyeong, Jihyeon and Kim, Jayoung and Jo, Minju and Seungji, Kook and Park, Noseong},
booktitle={International Conference on Learning Representations},
year={2022}
}
- Sheo Yon Jhin, Jaehoon Lee, Minju Jo, Seungji Kook, Jinsung Jeon, Jihyeon Hyeong, Jayoung Kim and Noseong Park, "EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting," The Web Conference (WWW), 2022. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3485447.3512030)[[Code]](https://github.com/sheoyon-jhin/EXIT) BibTeX
@inproceedings{jhin2022exit,
title={Exit: Extrapolation and interpolation-based neural controlled differential equations for time-series classification and forecasting},
author={Jhin, Sheo Yon and Lee, Jaehoon and Jo, Minju and Kook, Seungji and Jeon, Jinsung and Hyeong, Jihyeon and Kim, Jayoung and Park, Noseong},
booktitle={Proceedings of the ACM Web Conference 2022},
pages={3102--3112},
year={2022}
}
- Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park, "Graph Neural Controlled Differential Equations for Traffic Forecasting," AAAI Conference on Artificial Intelligence (AAAI), 2022. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/20587)[[Code]](https://github.com/jeongwhanchoi/STG-NCDE) BibTeX
@inproceedings{choi2022graph,
title={Graph neural controlled differential equations for traffic forecasting},
author={Choi, Jeongwhan and Choi, Hwangyong and Hwang, Jeehyun and Park, Noseong},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={36},
number={6},
pages={6367--6374},
year={2022}
}
- Taeyong Kong, Taeri Kim, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, and Sang-Wook Kim, "Linear, or Non-Linear, That is the Question!," ACM International WSDM Conference (WSDM), 2022. [[Paper]](https://dl.acm.org/doi/10.1145/3488560.3498501)[[Code]](https://github.com/qbxlvnf11/HMLET) BibTeX
@inproceedings{kong2022linear,
title={Linear, or non-linear, that is the question!},
author={Kong, Taeyong and Kim, Taeri and Jeon, Jinsung and Choi, Jeongwhan and Lee, Yeon-Chang and Park, Noseong and Kim, Sang-Wook},
booktitle={Proceedings of the fifteenth ACM international conference on web search and data mining},
pages={517--525},
year={2022}
}
### 2021
- Jaehoon Lee, Jihyeon Hyung, Jinsung Jeon, Noseong Park, and Jihoon Cho, "Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis," Conference on Neural Information Processing Systems (NeurIPS), 2021. [[Paper]](https://openreview.net/forum?id=tvDBe6K8L5o)[[Code]](https://github.com/leejaehoon2016/ITGAN) BibTeX
@article{lee2021invertible,
title={Invertible tabular GANs: Killing two birds with one stone for tabular data synthesis},
author={Lee, Jaehoon and Hyeong, Jihyeon and Jeon, Jinsung and Park, Noseong and Cho, Jihoon},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={4263--4273},
year={2021}
}
- Kanghyun Choi, Deokki Hong, Noseong Park, Youngsok Kim, and Jinho Lee, "Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples," Conference on Neural Information Processing Systems (NeurIPS), 2021. [[Paper]](https://openreview.net/forum?id=ejo1_Weiart)[[Code]](https://github.com/iamkanghyunchoi/qimera) BibTeX
@article{choi2021qimera,
title={Qimera: Data-free quantization with synthetic boundary supporting samples},
author={Choi, Kanghyun and Hong, Deokki and Park, Noseong and Kim, Youngsok and Lee, Jinho},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={14835--14847},
year={2021}
}
- Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, and Noseong Park, "Climate Modeling with Neural Diffusion Equations," IEEE International Conference on Data Mining (ICDM), 2021. [[Paper]](https://www.computer.org/csdl/proceedings-article/icdm/2021/239800a230/1Aqx1opaDPa)[[Code]](https://github.com/jeehyunHwang/Neural-Diffusion-Equation) BibTeX
@inproceedings{hwang2021climate,
title={Climate modeling with neural diffusion equations},
author={Hwang, Jeehyun and Choi, Jeongwhan and Choi, Hwangyong and Lee, Kookjin and Lee, Dongeun and Park, Noseong},
booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
pages={230--239},
year={2021},
organization={IEEE}
}
- Sheo Yon Jhin, Heejoo Shin, Seoyoung Hong, Solhee Park, and Noseong Park, "Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting," IEEE International Conference on Data Mining (ICDM), 2021. [[Paper]](https://www.computer.org/csdl/proceedings-article/icdm/2021/239800a250/1Aqx5oqTxwk)[[Code]](https://github.com/sheoyon-jhin/ANCDE) BibTeX
@inproceedings{jhin2021attentive,
title={Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting},
author={Jhin, Sheo Yon and Shin, Heejoo and Hong, Seoyoung and Jo, Minju and Park, Solhee and Park, Noseong and Lee, Seungbeom and Maeng, Hwiyoung and Jeon, Seungmin},
booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
pages={250--259},
year={2021},
organization={IEEE}
}
- Jinsung Jeon, Soyoung Kang, Minju Jo, Seunghyeon Cho, Noseong Park, Seonghoon Kim, and Chiyoung Song, "LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising," ACM International Conference on Information and Knowledge Management (CIKM), 2021. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3459637.3481935)[[Code]](https://github.com/jinsung-jeon/lightmove) BibTeX
@inproceedings{jeon2021lightmove,
title={Lightmove: A lightweight next-poi recommendation fortaxicab rooftop advertising},
author={Jeon, Jinsung and Kang, Soyoung and Jo, Minju and Cho, Seunghyeon and Park, Noseong and Kim, Seonghoon and Song, Chiyoung},
booktitle={Proceedings of the 30th ACM international conference on information \& knowledge management},
pages={3857--3866},
year={2021}
}
- Jeongwhan Choi, Jinsung Jeon, and Noseong Park, "LT-OCF: Learnable-Time ODE-based Collaborative Filtering," ACM International Conference on Information and Knowledge Management (CIKM), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3459637.3482449)[[Code]](https://github.com/jeongwhanchoi/LT-OCF) BibTeX
@inproceedings{choi2021ltocf,
title={{LT-OCF}: Learnable-time ode-based collaborative filtering},
author={Choi, Jeongwhan and Jeon, Jinsung and Park, Noseong},
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
pages={251--260},
year={2021}
}
- Sheo Yon Jhin, Minju Jo, Taeyong Kong, Jinsung Jeon, and Noseong Park, "ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3447548.3467419)[[Code]](https://github.com/sheoyon-jhin/ACE-NODE) BibTeX
@inproceedings{jhin2021ace,
title={Ace-node: Attentive co-evolving neural ordinary differential equations},
author={Jhin, Sheo Yon and Jo, Minju and Kong, Taeyong and Jeon, Jinsung and Park, Noseong},
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
pages={736--745},
year={2021}
}
- Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, and Noseong Park, "Large-Scale Data-Driven Airline Market Influence Maximization," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3447548.3467423)BibTeX
@inproceedings{li2021large,
title={Large-scale data-driven airline market influence maximization},
author={Li, Duanshun and Liu, Jing and Jeon, Jinsung and Hong, Seoyoung and Le, Thai and Lee, Dongwon and Park, Noseong},
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
pages={914--924},
year={2021}
}
- Duanshun Li, Jing Liu, Dongeun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, and Noseong Park, "A Novel Method to Solve Neural Knapsack Problems," International Conference on Machine Learning (ICML), 2021. [[Paper]](https://proceedings.mlr.press/v139/li21m.html)BibTeX
@inproceedings{li2021novel,
title={A novel method to solve neural knapsack problems},
author={Li, Duanshun and Liu, Jing and Lee, Dongeun and Seyedmazloom, Ali and Kaushik, Giridhar and Lee, Kookjin and Park, Noseong},
booktitle={International Conference on Machine Learning},
pages={6414--6424},
year={2021},
organization={PMLR}
}
- Thai Le, Noseong Park, and Dongwon Lee, "A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks," Annual Meeting of the Association for Computational Linguistics (ACL), 2021. [[Paper]](https://aclanthology.org/2021.acl-long.296/)[[Code]](https://github.com/lethaiq/ACL2021-DARCY-HoneypotDefenseNLP) BibTeX
@inproceedings{le2021sweet,
title={A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks},
author={Le, Thai and Park, Noseong and Lee, Dongwon},
booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
pages={3831--3844},
year={2021}
}
- Jayoung Kim, Jinsung Jeon, Jaehoon Lee, Jihyeon Hyung, and Noseong Park, "OCT-GAN: Neural ODE-based Conditional Tabular GANs," The Web Conference (WWW), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3442381.3449999)[[Code]](https://github.com/bigdyl-kaist/OCTGAN) BibTeX
@inproceedings{kim2021oct,
title={Oct-gan: Neural ode-based conditional tabular gans},
author={Kim, Jayoung and Jeon, Jinsung and Lee, Jaehoon and Hyeong, Jihyeon and Park, Noseong},
booktitle={Proceedings of the Web Conference 2021},
pages={1506--1515},
year={2021}
}
- Jinsung Jeon, Jing Liu, Jayoung Kim, Jaehoon Lee, Jamie Jooyeon Lee, Ozlem Uzuner, Sushil Jajodia, and Noseong Park, "Scalable Graph Synthesis with Adj and 1 – Adj," SIAM International Conference on Data Mining (SDM), 2021. [[Paper]](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.35) BibTeX
@inproceedings{jeon2021scalable,
title={Scalable Graph Synthesis with Adj and 1—Adj},
author={Jeon, Jinsung and Liu, Jing and Kim, Jayoung and Lee, Jaehoon and Park, Noseong and Lee, Jamie Jooyeon and Uzuner, Ozlem and Jajodia, Sushil},
booktitle={Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)},
pages={307--315},
year={2021},
organization={SIAM}
}
- Jinsung Jeon,Dongeun Lee, Seunghyun Hwang,Soyoung Kang, Duanshun Li, Kookjin Lee, Jing Liu, and Noseong Park, "Large-Scale Flight Frequency Optimization with Global Convergence in the US Domestic Air Passenger Markets," SIAM International Conference on Data Mining (SDM), 2021. [[Paper]](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.80) BibTeX
@@inproceedings{jeon2021large,
title={Large-scale flight frequency optimization with global convergence in the US domestic air passenger markets},
author={Jeon, Jinsung and Lee, Dongeun and Hwang, Seunghyun and Kang, Soyoung and Park, Noseong and Li, Duanshun and Lee, Kookjin and Liu, Jing},
booktitle={Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)},
pages={711--719},
year={2021},
organization={SIAM}
}
- Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jhin, and Noseong Park, "DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation," AAAI Conference on Artificial Intelligence (AAAI), 2021. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16992)[[Code]](https://github.com/jekim5418/DPM) BibTeX
@inproceedings{kim2021dpm,
title={DPM: A novel training method for physics-informed neural networks in extrapolation},
author={Kim, Jungeun and Lee, Kookjin and Lee, Dongeun and Jhin, Sheo Yon and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={9},
pages={8146--8154},
year={2021}
}
## Papers by Topic
### Time Series/Spatiotemporal Synthesis, Forecasting, and Anomaly Detection
- Seonkyu Lim, Jaehyeon Park, Seojin Kim, Hyowon Wi, Haksoo Lim, Jinsung Jeon, Jeongwhan Choi, and Noseong Park, "Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations," IEEE International Conference on Big Data (IEEE BigData), 2023. [[Paper]](https://www.computer.org/csdl/proceedings-article/bigdata/2023/10386388/1TUP4OY1IT6) BibTeX
@inproceedings{lim2023dnode,
title={Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations},
author={Lim, Seonkyu and Park, Jaehyeon and Kim, Seojin and Wi, Hyowon and Lim, Haksoo and Jeon, Jinsung and Choi, Jeongwhan and Park, Noseong},
booktitle={2023 IEEE International Conference on Big Data (BigData)},
pages={748--757},
year={2023},
organization={IEEE}
}
- Seonkyu Lim, Jeongwhan Choi, Noseong Park, Sang-Ha Yoon, Shinhyuck Kang, Young-Min Kim, and Hyunjoong Kang, "Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP," ACM International Conference on Information and Knowledge Management (CIKM), 2024.
- Sheo Yon Jhin, Seojin Kim, and Noseong Park, "Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024. [[Paper]](https://dl.acm.org/doi/10.1145/3637528.3671969)[[Code]](https://github.com/sheoyon-jhin/contime) BibTeX
@inproceedings{jhin2024contime,
title={Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization},
author={Jhin, Sheo Yon and Kim, Seojin and Park, Noseong},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={1234--1245},
year={2024}
}
- Hyowon Wi, Yehjin Shin, and Noseong Park, "Continuous-time Autoencoders for Regular and Irregular Time Series Imputation," ACM International Web Search and Data Mining Conference (WSDM), 2024. [[Paper]](https://dl.acm.org/doi/10.1145/3616855.3635831) BibTeX
@inproceedings{wi2024cta
author = {Wi, Hyowon and Shin, Yehjin and Park, Noseong},
title = {Continuous-time Autoencoders for Regular and Irregular Time Series Imputation},
year = {2024},
publisher = {Association for Computing Machinery},
doi = {10.1145/3616855.3635831},
booktitle = {Proceedings of the 17th ACM International Conference on Web Search and Data Mining},
pages = {826–835},
numpages = {10},
}
- Sheo Yon Jhin, Heejoo Shin, Sujie Kim, Seoyoung Hong, Solhee Park, Noseong Park, Seungbeom Lee, Hwiyoung Maeng, and Seungmin Jeon, "Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting," Knowledge and Information Systems (IF=3.205). [[Paper]](https://link.springer.com/article/10.1007/s10115-023-01977-5)[[Code]](https://github.com/sheoyon-jhin/ANCDE) BibTeX
@article{jhin2024attentive,
title={Attentive neural controlled differential equations for time-series classification and forecasting},
author={Jhin, Sheo Yon and Shin, Heejoo and Kim, Sujie and Hong, Seoyoung and Jo, Minju and Park, Solhee and Park, Noseong and Lee, Seungbeom and Maeng, Hwiyoung and Jeon, Seungmin},
journal={Knowledge and Information Systems},
volume={66},
number={3},
pages={1885--1915},
year={2024},
publisher={Springer}
}
- Sheo Yon Jhin, Minju Jo, Seungji Kook, and Noseong Park, "Learnable Path in Neural Controlled Differential Equations," AAAI Conference on Artificial Intelligence (AAAI), 2023. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29036)BibTeX
@inproceedings{jhin2023leap,
title={Learnable path in neural controlled differential equations},
author={Jhin, Sheo Yon and Jo, Minju and Kook, Seungji and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={7},
pages={8014--8022},
year={2023}
}
- Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park, "HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts," NeurIPS Workshop on Distribution Shifts (DistShift), 2023. [[Paper]](https://openreview.net/forum?id=7yVBYSPI8Z)BibTeX
@inproceedings{lee2024hypergpa,
title={HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts},
author={Jaehoon Lee and Chan Kim and Gyumin Lee and Haksoo Lim and Jeongwhan Choi and Kookjin Lee and Dongeun Lee and Sanghyun Hong and Noseong Park},
booktitle={NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models},
year={2024},
url={https://openreview.net/forum?id=7yVBYSPI8Z}
}
- Sheo Yon Jhin, Jaehoon Lee, and Noseong Park, "Precursor-of-Anomaly Detection for Irregular Time Series," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3580305.3599469)[[Code]](https://github.com/sheoyon-jhin/pad) BibTeX
@inproceedings{jhin2023precursor,
title={Precursor-of-anomaly detection for irregular time series},
author={Jhin, Sheo Yon and Lee, Jaehoon and Park, Noseong},
booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={917--929},
year={2023}
}
- Minju Jo, Seungji Kook, and Noseong Park, "Hawkes Process based on Controlled Differential Equations," International Joint Conference on Artificial Intelligence (IJCAI), 2023. [[Paper]](https://www.ijcai.org/proceedings/2023/239)[[Code]](https://github.com/kookseungji/Hawkes-Process-Based-on-Controlled-Differential-Equations) BibTeX
@inproceedings{jo2023hawkes,
title={Hawkes process based on controlled differential equations},
author={Jo, Minju and Kook, Seungji and Park, Noseong},
booktitle={Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence},
pages={2151--2159},
year={2023}
}
- Jeongwhan Choi, and Noseong Park, "Graph Neural Rough Differential Equations for Traffic Forecasting," ACM Transactions on Intelligent Systems and Technology (IF=10.489), Vol. 14, No. 4, 2023. [[Paper]](https://dl.acm.org/doi/10.1145/3604808)[[Code]](https://github.com/jeongwhanchoi/STG-NRDE) BibTeX
@article{choi2023graph,
title={Graph neural rough differential equations for traffic forecasting},
author={Choi, Jeongwhan and Park, Noseong},
journal={ACM Transactions on Intelligent Systems and Technology},
volume={14},
number={4},
pages={1--27},
year={2023},
publisher={ACM New York, NY}
}
- Sheo Yon Jhin, Jaehoon Lee, Minju Jo, Seungji Kook, Jinsung Jeon, Jihyeon Hyeong, Jayoung Kim and Noseong Park, "EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting," The Web Conference (WWW), 2022. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3485447.3512030)[[Code]](https://github.com/sheoyon-jhin/EXIT) BibTeX
@inproceedings{jhin2022exit,
title={Exit: Extrapolation and interpolation-based neural controlled differential equations for time-series classification and forecasting},
author={Jhin, Sheo Yon and Lee, Jaehoon and Jo, Minju and Kook, Seungji and Jeon, Jinsung and Hyeong, Jihyeon and Kim, Jayoung and Park, Noseong},
booktitle={Proceedings of the ACM Web Conference 2022},
pages={3102--3112},
year={2022}
}
- Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park, "Graph Neural Controlled Differential Equations for Traffic Forecasting," AAAI Conference on Artificial Intelligence (AAAI), 2022. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/20587)[[Code]](https://github.com/jeongwhanchoi/STG-NCDE) BibTeX
@inproceedings{choi2022graph,
title={Graph neural controlled differential equations for traffic forecasting},
author={Choi, Jeongwhan and Choi, Hwangyong and Hwang, Jeehyun and Park, Noseong},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={36},
number={6},
pages={6367--6374},
year={2022}
}
- Sheo Yon Jhin, Heejoo Shin, Seoyoung Hong, Solhee Park, and Noseong Park, "Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting," IEEE International Conference on Data Mining (ICDM), 2021. [[Paper]](https://www.computer.org/csdl/proceedings-article/icdm/2021/239800a250/1Aqx5oqTxwk)[[Code]](https://github.com/sheoyon-jhin/ANCDE) BibTeX
@inproceedings{jhin2021attentive,
title={Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting},
author={Jhin, Sheo Yon and Shin, Heejoo and Hong, Seoyoung and Jo, Minju and Park, Solhee and Park, Noseong and Lee, Seungbeom and Maeng, Hwiyoung and Jeon, Seungmin},
booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
pages={250--259},
year={2021},
organization={IEEE}
}
- Sheo Yon Jhin, Minju Jo, Taeyong Kong, Jinsung Jeon, and Noseong Park, "ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3447548.3467419)[[Code]](https://github.com/sheoyon-jhin/ACE-NODE) BibTeX
@inproceedings{jhin2021ace,
title={Ace-node: Attentive co-evolving neural ordinary differential equations},
author={Jhin, Sheo Yon and Jo, Minju and Kong, Taeyong and Jeon, Jinsung and Park, Noseong},
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
pages={736--745},
year={2021}
}
### Recommendation (Collaborative Filtering and Sequential Recommendation)
- Seoyoung Hong, Jeongwhan Choi, Yeon-Chang Lee, Srijan Kumar, and Noseong Park, "SVD-AE: Simple Autoencoders for Collaborative Filtering," International Joint Conference on Artificial Intelligence (IJCAI), 2024. [[Paper]](https://www.ijcai.org/proceedings/2024/227)[[Code]](https://github.com/seoyoungh/svd-ae) BibTeX
@inproceedings{hong2024svdae,
title = {SVD-AE: Simple Autoencoders for Collaborative Filtering},
author = {Hong, Seoyoung and Choi, Jeongwhan and Lee, Yeon-Chang and Kumar, Srijan and Park, Noseong},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {2054--2062},
year = {2024},
doi = {10.24963/ijcai.2024/227},
url = {https://doi.org/10.24963/ijcai.2024/227},
}
- Yehjin Shin, Jeongwhan Choi, Hyowon Wi, and Noseong Park, "An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention," AAAI Conference on Artificial Intelligence (AAAI), 2024. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/28747)[[Code]](https://github.com/yehjin-shin/BSARec) BibTeX
@inproceedings{shin2024bsarec,
title={An attentive inductive bias for sequential recommendation beyond the self-attention},
author={Shin, Yehjin and Choi, Jeongwhan and Wi, Hyowon and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={8},
pages={8984--8992},
year={2024}
}
- Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho, "Blurring-Sharpening Process Models for Collaborative Filtering," International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3539618.3591645)[[Code]](https://github.com/jeongwhanchoi/BSPM) BibTeX
@inproceedings{choi2023bspm,
title={Blurring-Sharpening Process Models for Collaborative Filtering},
author={Choi, Jeongwhan and Hong, Seoyoung and Park, Noseong and Cho, Sung-Bae},
booktitle={Proceedings of the ACM Conference on Research and Development in Information Retrieval (SIGIR)},
year={2023}
}
- Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, and Noseong Park, "TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering," IEEE International Conference on Big Data (IEEE BigData), 2022 [Best Student Paper Award].[[Paper]](https://ieeexplore.ieee.org/abstract/document/10020773) BibTeX
@inproceedings{hong2022timekit,
title={{TimeKit}: A time-series forecasting-based upgrade kit for collaborative filtering},
author={Hong, Seoyoung and Jo, Minju and Kook, Seungji and Jung, Jaeeun and Wi, Hyowon and Park, Noseong and Cho, Sung-Bae},
booktitle={2022 IEEE International Conference on Big Data (Big Data)},
pages={565--574},
year={2022},
organization={IEEE}
}
- Taeyong Kong, Taeri Kim, Jinsung Jeon, Jeongwhan Choi, Yeon-Chang Lee, Noseong Park, and Sang-Wook Kim, "Linear, or Non-Linear, That is the Question!," ACM International WSDM Conference (WSDM), 2022. [[Paper]](https://dl.acm.org/doi/10.1145/3488560.3498501)[[Code]](https://github.com/qbxlvnf11/HMLET) BibTeX
@inproceedings{kong2022linear,
title={Linear, or non-linear, that is the question!},
author={Kong, Taeyong and Kim, Taeri and Jeon, Jinsung and Choi, Jeongwhan and Lee, Yeon-Chang and Park, Noseong and Kim, Sang-Wook},
booktitle={Proceedings of the fifteenth ACM international conference on web search and data mining},
pages={517--525},
year={2022}
}
- Jinsung Jeon, Soyoung Kang, Minju Jo, Seunghyeon Cho, Noseong Park, Seonghoon Kim, and Chiyoung Song, "LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising," ACM International Conference on Information and Knowledge Management (CIKM), 2021. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3459637.3481935)[[Code]](https://github.com/jinsung-jeon/lightmove) BibTeX
@inproceedings{jeon2021lightmove,
title={Lightmove: A lightweight next-poi recommendation fortaxicab rooftop advertising},
author={Jeon, Jinsung and Kang, Soyoung and Jo, Minju and Cho, Seunghyeon and Park, Noseong and Kim, Seonghoon and Song, Chiyoung},
booktitle={Proceedings of the 30th ACM international conference on information \& knowledge management},
pages={3857--3866},
year={2021}
}
- Jeongwhan Choi, Jinsung Jeon, and Noseong Park, "LT-OCF: Learnable-Time ODE-based Collaborative Filtering," ACM International Conference on Information and Knowledge Management (CIKM), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3459637.3482449)[[Code]](https://github.com/jeongwhanchoi/LT-OCF) BibTeX
@inproceedings{choi2021ltocf,
title={{LT-OCF}: Learnable-time ode-based collaborative filtering},
author={Choi, Jeongwhan and Jeon, Jinsung and Park, Noseong},
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
pages={251--260},
year={2021}
}
### Tabular Data Synthesis and Representation Learning
- Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, and Noseong Park, "Polynomial-based Self-Attention for Table Representation Learning," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/kim24ae.html) BibTeX
@InProceedings{kim24tabpsa,
title = {Polynomial-based Self-Attention for Table Representation Learning},
author = {Kim, Jayoung and Shin, Yehjin and Choi, Jeongwhan and Wi, Hyowon and Park, Noseong},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {24509--24526},
year = {2024},
volume = {235},
}
- Chaejeong Lee, Jayoung Kim, and Noseong Park, "CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis," International Conference on Machine Learning (ICML), 2023. [[Paper]](https://proceedings.mlr.press/v202/lee23i.html)[[Code]](https://github.com/chaejeonglee/codi) BibTeX
@inproceedings{lee2023codi,
title={Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis},
author={Lee, Chaejeong and Kim, Jayoung and Park, Noseong},
booktitle={International Conference on Machine Learning},
pages={18940--18956},
year={2023},
organization={PMLR}
}
- Jayoung Kim, Chaejeong Lee, and Noseong Park, "STaSy: Score-based Tabular Data Synthesis," International Conference on Learning Representations (ICLR), 2023. [[Paper]](https://openreview.net/forum?id=1mNssCWt_v)[[Code]](https://github.com/JayoungKim408/STaSy) BibTeX
@inproceedings{kim2024stasy,
title={STaSy: Score-based Tabular data Synthesis},
author={Kim, Jayoung and Lee, Chaejeong and Park, Noseong},
year={2023},
booktitle={The Eleventh International Conference on Learning Representations}
}
- Jaehoon Lee, Jihyeon Hyung, Jinsung Jeon, Noseong Park, and Jihoon Cho, "Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis," Conference on Neural Information Processing Systems (NeurIPS), 2021. [[Paper]](https://openreview.net/forum?id=tvDBe6K8L5o)[[Code]](https://github.com/leejaehoon2016/ITGAN) BibTeX
@article{lee2021invertible,
title={Invertible tabular GANs: Killing two birds with one stone for tabular data synthesis},
author={Lee, Jaehoon and Hyeong, Jihyeon and Jeon, Jinsung and Park, Noseong and Cho, Jihoon},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={4263--4273},
year={2021}
}
- Jihyeon Hyeong, Jayoung Kim, Noseong Park, and Sushil Jajodia, "An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models," ACM International Conference on Information and Knowledge Management (CIKM), 2022, Short Research Paper. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3511808.3557546)[[Code]](https://github.com/jayoungkim408/mia) BibTeX
@inproceedings{hyeong2022empirical,
title={An empirical study on the membership inference attack against tabular data synthesis models},
author={Hyeong, Jihyeon and Kim, Jayoung and Park, Noseong and Jajodia, Sushil},
booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
pages={4064--4068},
year={2022}
}
- Jayoung Kim, ChaeJeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, and Jihoon Cho, "SOS: Score-based Oversampling Minor Classes for Tabular Data," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3534678.3539454)[[Code]](https://github.com/JayoungKim408/SOS) BibTeX
@inproceedings{kim2022sos,
title={Sos: Score-based oversampling for tabular data},
author={Kim, Jayoung and Lee, Chaejeong and Shin, Yehjin and Park, Sewon and Kim, Minjung and Park, Noseong and Cho, Jihoon},
booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={762--772},
year={2022}
}
- Jayoung Kim, Jinsung Jeon, Jaehoon Lee, Jihyeon Hyung, and Noseong Park, "OCT-GAN: Neural ODE-based Conditional Tabular GANs," The Web Conference (WWW), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3442381.3449999)[[Code]](https://github.com/bigdyl-kaist/OCTGAN) BibTeX
@inproceedings{kim2021oct,
title={Oct-gan: Neural ode-based conditional tabular gans},
author={Kim, Jayoung and Jeon, Jinsung and Lee, Jaehoon and Hyeong, Jihyeon and Park, Noseong},
booktitle={Proceedings of the Web Conference 2021},
pages={1506--1515},
year={2021}
}
### Differential Equation-based Deep Learning and Physics-informed Neural Networks
- Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park, "Parameterized Physics-informed Neural Networks for Parameterized PDEs," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/cho24b.html)[[Code]](https://github.com/WooJin-Cho/Parameterized-Physics-informed-Neural-Networks) BibTeX
@inproceedings{cho2024p2inn,
title = {Parameterized Physics-informed Neural Networks for Parameterized {PDE}s},
author = {Cho, Woojin and Jo, Minju and Lim, Haksoo and Lee, Kookjin and Lee, Dongeun and Hong, Sanghyun and Park, Noseong},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {8510--8533},
year = {2024},
volume = {235},
publisher = {PMLR},
}
- Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park, "Extension of Physics-informed Neural Networks to Solving Parameterized PDEs," ICLR Workshop on AI4DifferentialEquations in Science (AI4DiffEqtnsInSci), 2024. [[Paper]](https://openreview.net/forum?id=EAkRlHFLBc&referrer=%5Bthe%20profile%20of%20Noseong%20Park%5D(%2Fprofile%3Fid%3D~Noseong_Park1))BibTeX
@inproceedings{cho2024extension,
title={Extension of Physics-informed Neural Networks to Solving Parameterized {PDE}s},
author={Woojin Cho and Minju Jo and Haksoo Lim and Kookjin Lee and Dongeun Lee and Sanghyun Hong and Noseong Park},
booktitle={ICLR 2024 Workshop on AI4DifferentialEquations In Science},
year={2024},
url={https://openreview.net/forum?id=EAkRlHFLBc}
}
- Woojin Cho, Seunghyeon Cho, Hyundong Jin, Jinsung Jeon, Kookjin Lee, Sanghyun Hong, Dongeun Lee, Jonghyun Choi, and Noseong Park, "Operator-learning-inspired Modeling of Neural Ordinary Differential Equations," AAAI Conference on Artificial Intelligence (AAAI), 2024. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29036)[[Code]](https://github.com/WooJin-Cho/BFNO-NODE) BibTeX
@inproceedings{cho2024bfno,
title={Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations},
author={Cho, Woojin and Cho, Seunghyeon and Jin, Hyundong and Jeon, Jinsung and Lee, Kookjin and Hong, Sanghyun and Lee, Dongeun and Choi, Jonghyun and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={10},
pages={11543--11551},
year={2024}
}
- Woojin Cho, Kookjin Lee, Donsub Rim, and Noseong Park, "Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks," Conference on Neural Information Processing Systems (NeurIPS), 2023. [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/24f8dd1b8f154f1ee0d7a59e368eccf3-Abstract-Conference.html)[[Code]](https://github.com/WooJin-Cho/Hyper-LR-PINN) BibTeX
@inproceedings{cho2023hyper,
author = {Cho, Woojin and Lee, Kookjin and Rim, Donsub and Park, Noseong},
title = {Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks},
booktitle = {Advances in Neural Information Processing Systems},
pages = {11219--11231},
volume = {36},
year = {2023}
}
- Minju Jo, Seungji Kook, and Noseong Park, "Hawkes Process based on Controlled Differential Equations," International Joint Conference on Artificial Intelligence (IJCAI), 2023. [[Paper]](https://www.ijcai.org/proceedings/2023/239)[[Code]](https://github.com/kookseungji/Hawkes-Process-Based-on-Controlled-Differential-Equations) BibTeX
@inproceedings{jo2023hawkes,
title={Hawkes process based on controlled differential equations},
author={Jo, Minju and Kook, Seungji and Park, Noseong},
booktitle={Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence},
pages={2151--2159},
year={2023}
}
- Jeongwhan Choi, and Noseong Park, "Graph Neural Rough Differential Equations for Traffic Forecasting," ACM Transactions on Intelligent Systems and Technology (IF=10.489), Vol. 14, No. 4, 2023. [[Paper]](https://dl.acm.org/doi/10.1145/3604808)[[Code]](https://github.com/jeongwhanchoi/STG-NRDE) BibTeX
@article{choi2023graph,
title={Graph neural rough differential equations for traffic forecasting},
author={Choi, Jeongwhan and Park, Noseong},
journal={ACM Transactions on Intelligent Systems and Technology},
volume={14},
number={4},
pages={1--27},
year={2023},
publisher={ACM New York, NY}
}
- Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, and Noseong Park, "Climate Modeling with Neural Advection-Diffusion Equations," Knowledge and Information Systems (IF=3.205). [[Paper]](https://link.springer.com/article/10.1007/s10115-023-01829-2)[[Code]](https://github.com/hwangyong753/NADE) BibTeX
@article{choi2023climate,
title={Climate modeling with neural advection--diffusion equation},
author={Choi, Hwangyong and Choi, Jeongwhan and Hwang, Jeehyun and Lee, Kookjin and Lee, Dongeun and Park, Noseong},
journal={Knowledge and Information Systems},
volume={65},
number={6},
pages={2403--2427},
year={2023},
publisher={Springer}
}
- Seunghyeon Cho, Sanghyun Hong, Kookjin Lee, and Noseong Park, "AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation," ICML Workshop on Continuous Time Methods for Machine Learning, 2022. [[Paper]](https://arxiv.org/abs/2207.06066)[[Code]](https://github.com/Seunghyeon-Cho/AdamNODE) BibTeX
@article{cho2022adamnodes,
title={AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation},
author={Cho, Suneghyeon and Hong, Sanghyun and Lee, Kookjin and Park, Noseong},
journal={arXiv preprint arXiv:2207.06066},
year={2022}
}
- Jaehoon Lee, Jinsung Jeon, Sheo yon Jhin, Jihyeon Hyeong, Jayoung Kim, Minju Jo, Seungji Kook, and Noseong Park, "LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations," International Conference on Learning Representations (ICLR), 2022. [[Paper]](https://openreview.net/forum?id=fCG75wd39ze)[[Code]](https://github.com/leejaehoon2016/LORD) BibTeX
@inproceedings{lee2022lord,
title={{LORD}: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations},
author={LEE, JAEHOON and Jeon, Jinsung and yon Jhin, Sheo and Hyeong, Jihyeon and Kim, Jayoung and Jo, Minju and Seungji, Kook and Park, Noseong},
booktitle={International Conference on Learning Representations},
year={2022}
}
- Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, and Noseong Park, "Climate Modeling with Neural Diffusion Equations," IEEE International Conference on Data Mining (ICDM), 2021. [[Paper]](https://www.computer.org/csdl/proceedings-article/icdm/2021/239800a230/1Aqx1opaDPa)[[Code]](https://github.com/jeehyunHwang/Neural-Diffusion-Equation) BibTeX
@inproceedings{hwang2021climate,
title={Climate modeling with neural diffusion equations},
author={Hwang, Jeehyun and Choi, Jeongwhan and Choi, Hwangyong and Lee, Kookjin and Lee, Dongeun and Park, Noseong},
booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
pages={230--239},
year={2021},
organization={IEEE}
}
- Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jhin, and Noseong Park, "DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation," AAAI Conference on Artificial Intelligence (AAAI), 2021. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16992)[[Code]](https://github.com/jekim5418/DPM) BibTeX
@inproceedings{kim2021dpm,
title={DPM: A novel training method for physics-informed neural networks in extrapolation},
author={Kim, Jungeun and Lee, Kookjin and Lee, Dongeun and Jhin, Sheo Yon and Park, Noseong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={9},
pages={8146--8154},
year={2021}
}
### Graph Convolutional Networks
- Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, and Noseong Park, "PANDA: Expanded Width-Aware Message Passing Beyond Rewiring," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/choi24f.html)[[Code]](https://github.com/jeongwhanchoi/panda) BibTeX
@inproceedings{choi2024panda,
title = {{PANDA}: Expanded Width-Aware Message Passing Beyond Rewiring},
author = {Choi, Jeongwhan and Park, Sumin and Wi, Hyowon and Cho, Sung-Bae and Park, Noseong},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {8740--8761},
year = {2024},
volume = {235},
publisher = {PMLR},
}
- Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee, Nayong Kim, Kiseok Chang, Changseung Woo, Ilho Kim, Seokwoo Lee, Joon Young Yang, Sooyoung Yoon, and Noseong Park, "Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer," International Conference on Learning Representations (ICLR), 2024. [[Paper]](https://openreview.net/forum?id=90yw2uM6J5)[[Code]](https://github.com/yuyudeep/hcmt) BibTeX
@inproceedings{
yu2024learning,
title={Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer},
author={Youn-Yeol Yu and Jeongwhan Choi and Woojin Cho and Kookjin Lee and Nayong Kim and Kiseok Chang and ChangSeung Woo and Ilho Kim and SeokWoo Lee and Joon Young Yang and Sooyoung Yoon and Noseong Park},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=90yw2uM6J5}
}
- Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho, "GREAD: Graph Neural Reaction-Diffusion Networks," International Conference on Machine Learning (ICML), 2023. [[Paper]](https://proceedings.mlr.press/v202/choi23a.html)[[Code]](https://github.com/jeongwhanchoi/gread) BibTeX
@inproceedings{choi2023gread,
title={{GREAD}: Graph neural reaction-diffusion networks},
author={Choi, Jeongwhan and Hong, Seoyoung and Park, Noseong and Cho, Sung-Bae},
booktitle={International Conference on Machine Learning},
pages={5722--5747},
year={2023},
organization={PMLR}
}
- Manh Tuan Do, Noseong Park, and Kijung Shin, "Two-Stage Training of Graph Neural Networks for Graph Classification," to appear in Neural Processing Letters (IF=2.565). [[Paper]](https://link.springer.com/article/10.1007/s11063-022-10985-5)[[Code]](https://github.com/manhtuando97/two-stage-gnn) BibTeX
@article{do2023two,
title={Two-stage training of graph neural networks for graph classification},
author={Do, Manh Tuan and Park, Noseong and Shin, Kijung},
journal={Neural Processing Letters},
volume={55},
number={3},
pages={2799--2823},
year={2023},
publisher={Springer}
}
- Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park, "Graph Neural Controlled Differential Equations for Traffic Forecasting," AAAI Conference on Artificial Intelligence (AAAI), 2022. [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/20587)[[Code]](https://github.com/jeongwhanchoi/STG-NCDE) BibTeX
@inproceedings{choi2022graph,
title={Graph neural controlled differential equations for traffic forecasting},
author={Choi, Jeongwhan and Choi, Hwangyong and Hwang, Jeehyun and Park, Noseong},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={36},
number={6},
pages={6367--6374},
year={2022}
}
### Deep Generative Models
- Jinsung Jeon and Noseong Park, "SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations," ICLR Workshop on Practical ML for Limited/Low-resource Settings (PML4LRS), 2024. [[Paper]](https://openreview.net/forum?id=01sjjq6m4F)BibTeX
@inproceedings{jeon2024spigan,
title={{SPI}-{GAN}: Denoising Diffusion {GAN}s with Straight-Path Interpolations},
author={Jinsung Jeon and Noseong Park},
booktitle={5th Workshop on practical ML for limited/low resource settings},
year={2024},
url={https://openreview.net/forum?id=01sjjq6m4F}
}
- Haksoo Lim, Sewon Park, Minjung Kim, Jaehoon Lee, Seonkyu Lim, and Noseong Park, "MadSGM: Multivariate Anomaly Detection with Score-based Generative Models," ACM International Conference on Information and Knowledge Management (CIKM), 2023. [[Paper]](https://dl.acm.org/doi/10.1145/3583780.3614956) BibTeX
@inproceedings{lim2023madsgm,
title={Madsgm: Multivariate anomaly detection with score-based generative models},
author={Lim, Haksoo and Park, Sewon and Kim, Minjung and Lee, Jaehoon and Lim, Seonkyu and Park, Noseong},
booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages={1411--1420},
year={2023}
}
- Jinsung Jeon, Jeonghak Kim, Haryong Song, Seunghyeon Cho, and Noseong Park, "GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks," Conference on Neural Information Processing Systems (NeurIPS), 2022. [[Paper]](https://openreview.net/forum?id=ez6VHWvuXEx)[[Code]](https://github.com/Jinsung-Jeon/GT-GAN) BibTeX
@article{jeon2022gt,
title={GT-GAN: General purpose time series synthesis with generative adversarial networks},
author={Jeon, Jinsung and Kim, Jeonghak and Song, Haryong and Cho, Seunghyeon and Park, Noseong},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={36999--37010},
year={2022}
}
### Miscellaneous
- Fan Wu, Woojin Cho, David Korotky, Sanghyun Hong, Donsub Rim, Noseong Park, and Kookjin Lee, "Identifying Contemporaneous and Lagged Dependence Structures by Promoting Sparsity in Continuous-time Neural Networks," ACM International Conference on Information and Knowledge Management (CIKM), 2024.
- Hyeyoon Lee, Kanghyun Choi, Dain Kwon, SunJong Park, Mayoore Selvarasa Jaiswal, Noseong Park, Jonghyun Choi, and Jinho Lee, "DataFreeShield: Defending Adversarial Attacks without Training Data," International Conference on Machine Learning (ICML), 2024. [[Paper]](https://proceedings.mlr.press/v235/lee24f.html)[[Code]](https://github.com/hylee817/datafreeshield) BibTeX
@inproceedings{lee2024datafreeshield,
title={DataFreeShield: Defending Adversarial Attacks without Training Data},
author={Lee, Hyeyoon and Choi, Kanghyun and Kwon, Dain and Park, Sunjong and Jaiswal, Mayoore Selvarasa and Park, Noseong and Choi, Jonghyun and Lee, Jinho},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2024}
}
- Jinsung Jeon, Hyundong Jin, Jonghyun Choi, Sanghyun Hong, Dongeun Lee, Kookjin Lee, and Noseong Park, "PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images," International Conference on Learning Representations (ICLR), 2024. [[Paper]](https://openreview.net/forum?id=Cf4FJGmHRQ) BibTeX
@inproceedings{jeon2024pacfno,
title={{PAC}-{FNO}: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images},
author={Jinsung Jeon and Hyundong Jin and Jonghyun Choi and Sanghyun Hong and Dongeun Lee and Kookjin Lee and Noseong Park},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Cf4FJGmHRQ}
}
- JaeYeon Park, Kichang Lee, Noseong Park, Seng Chan You, and JeongGil Ko, "Self-Attention LSTM-FCN Model for Arrhythmia Classification and Uncertainty Assessment," to appear in Artificial Intelligence In Medicine (IF=7.011), 2023. [[Paper]](https://www.sciencedirect.com/science/article/pii/S0933365723000842) BibTeX
@article{park2023self,
title={Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment},
author={Park, JaeYeon and Lee, Kichang and Park, Noseong and You, Seng Chan and Ko, JeongGil},
journal={Artificial Intelligence in Medicine},
volume={142},
pages={102570},
year={2023},
publisher={Elsevier}
}
- Jaewon Jung, Jaeyong Song, Hongsun Jang, Hyeyoon Lee, Kanghyun Choi, Noseong Park, and Jinho Lee, "Fast Adversarial Training with Dynamic Batch-level Attack Control," Design Automation Conference (DAC), 2023. [[Paper]](https://ieeexplore.ieee.org/document/10247930) BibTeX
@inproceedings{jung2023fast,
title={Fast Adversarial Training with Dynamic Batch-level Attack Control},
author={Jung, Jaewon and Song, Jaeyong and Jang, Hongsun and Lee, Hyeyoon and Choi, Kanghyun and Park, Noseong and Lee, Jinho},
booktitle={2023 60th ACM/IEEE Design Automation Conference (DAC)},
pages={1--6},
year={2023},
organization={IEEE}
}
- Taeri Kim, Noseong Park, Jiwon Hong, and Sang-Wook Kim, "Phishing URL Detection: A Network-based Approach Robust to Evasion," ACM Conference on Computer and Communications Security (CCS), 2022. [[Paper]](https://dl.acm.org/doi/10.1145/3548606.3560615)[[Code]](https://github.com/taerikkk/bpe) BibTeX
@inproceedings{kim2022phishing,
title={Phishing url detection: A network-based approach robust to evasion},
author={Kim, Taeri and Park, Noseong and Hong, Jiwon and Kim, Sang-Wook},
booktitle={Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security},
pages={1769--1782},
year={2022}
}
- Seoyoung Hong, Heejoo Shin, Jeongwhan Choi, and Noseong Park, "Prediction-based One-shot Dynamic Parking Pricing," ACM International Conference on Information and Knowledge Management (CIKM), 2022, Full Research Paper. [[Paper]](https://dl.acm.org/doi/abs/10.1145/3511808.3557421)[[Code]](https://github.com/seoyoungh/one-shot-optimization) BibTeX
@inproceedings{hong2022prediction,
title={Prediction-based one-shot dynamic parking pricing},
author={Hong, Seoyoung and Shin, Heejoo and Choi, Jeongwhan and Park, Noseong},
booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
pages={748--757},
year={2022}
}
- Kanghyun Choi, Hye Yoon Lee, Deokki Hong, Joonsang Yu, Noseong Park, Youngsok Kim, and Jinho Lee, "It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [[Paper]](https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.html)[[Code]](https://github.com/iamkanghyunchoi/ait) BibTeX
@inproceedings{choi2022s,
title={It's all in the teacher: Zero-shot quantization brought closer to the teacher},
author={Choi, Kanghyun and Lee, Hye Yoon and Hong, Deokki and Yu, Joonsang and Park, Noseong and Kim, Youngsok and Lee, Jinho},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8311--8321},
year={2022}
}
- Thai Le, Noseong Park, and Dongwon Lee, "SHIELD: Defending Textual Neural Networks against Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher," Annual Meeting of the Association for Computational Linguistics (ACL), 2022. [[Paper]](https://aclanthology.org/2022.acl-long.459/)[[Code]](https://github.com/lethaiq/shield-defend-adversarial-texts) BibTeX
@inproceedings{le2022shield,
title={SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher},
author={Le, Thai and Park, Noseong and Lee, Dongwon},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={6661--6674},
year={2022}
}
- Deokki Hong, Kanghyun Choi, Hey Yoon Lee, Joonsang Yu, Youngsok Kim, Noseong Park, and Jinho Lee, "Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration," Design Automation Conference (DAC), 2022. [[Paper]](https://dl.acm.org/doi/10.1145/3489517.3530507) BibTeX
@inproceedings{hong2022enabling,
title={Enabling hard constraints in differentiable neural network and accelerator co-exploration},
author={Hong, Deokki and Choi, Kanghyun and Lee, Hye Yoon and Yu, Joonsang and Park, Noseong and Kim, Youngsok and Lee, Jinho},
booktitle={Proceedings of the 59th ACM/IEEE Design Automation Conference},
pages={589--594},
year={2022}
}
- Kanghyun Choi, Deokki Hong, Noseong Park, Youngsok Kim, and Jinho Lee, "Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples," Conference on Neural Information Processing Systems (NeurIPS), 2021. [[Paper]](https://openreview.net/forum?id=ejo1_Weiart)[[Code]](https://github.com/iamkanghyunchoi/qimera) BibTeX
@article{choi2021qimera,
title={Qimera: Data-free quantization with synthetic boundary supporting samples},
author={Choi, Kanghyun and Hong, Deokki and Park, Noseong and Kim, Youngsok and Lee, Jinho},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={14835--14847},
year={2021}
}
- Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, and Noseong Park, "Large-Scale Data-Driven Airline Market Influence Maximization," ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. [[Paper]](https://dl.acm.org/doi/10.1145/3447548.3467423)BibTeX
@inproceedings{li2021large,
title={Large-scale data-driven airline market influence maximization},
author={Li, Duanshun and Liu, Jing and Jeon, Jinsung and Hong, Seoyoung and Le, Thai and Lee, Dongwon and Park, Noseong},
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
pages={914--924},
year={2021}
}
- Duanshun Li, Jing Liu, Dongeun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, and Noseong Park, "A Novel Method to Solve Neural Knapsack Problems," International Conference on Machine Learning (ICML), 2021. [[Paper]](https://proceedings.mlr.press/v139/li21m.html)BibTeX
@inproceedings{li2021novel,
title={A novel method to solve neural knapsack problems},
author={Li, Duanshun and Liu, Jing and Lee, Dongeun and Seyedmazloom, Ali and Kaushik, Giridhar and Lee, Kookjin and Park, Noseong},
booktitle={International Conference on Machine Learning},
pages={6414--6424},
year={2021},
organization={PMLR}
}
- Thai Le, Noseong Park, and Dongwon Lee, "A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks," Annual Meeting of the Association for Computational Linguistics (ACL), 2021. [[Paper]](https://aclanthology.org/2021.acl-long.296/)[[Code]](https://github.com/lethaiq/ACL2021-DARCY-HoneypotDefenseNLP) BibTeX
@inproceedings{le2021sweet,
title={A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks},
author={Le, Thai and Park, Noseong and Lee, Dongwon},
booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
pages={3831--3844},
year={2021}
}
- Jinsung Jeon, Jing Liu, Jayoung Kim, Jaehoon Lee, Jamie Jooyeon Lee, Ozlem Uzuner, Sushil Jajodia, and Noseong Park, "Scalable Graph Synthesis with Adj and 1 – Adj," SIAM International Conference on Data Mining (SDM), 2021. [[Paper]](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.35) BibTeX
@inproceedings{jeon2021scalable,
title={Scalable Graph Synthesis with Adj and 1—Adj},
author={Jeon, Jinsung and Liu, Jing and Kim, Jayoung and Lee, Jaehoon and Park, Noseong and Lee, Jamie Jooyeon and Uzuner, Ozlem and Jajodia, Sushil},
booktitle={Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)},
pages={307--315},
year={2021},
organization={SIAM}
}
- Jinsung Jeon,Dongeun Lee, Seunghyun Hwang,Soyoung Kang, Duanshun Li, Kookjin Lee, Jing Liu, and Noseong Park, "Large-Scale Flight Frequency Optimization with Global Convergence in the US Domestic Air Passenger Markets," SIAM International Conference on Data Mining (SDM), 2021. [[Paper]](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.80) BibTeX
@@inproceedings{jeon2021large,
title={Large-scale flight frequency optimization with global convergence in the US domestic air passenger markets},
author={Jeon, Jinsung and Lee, Dongeun and Hwang, Seunghyun and Kang, Soyoung and Park, Noseong and Li, Duanshun and Lee, Kookjin and Liu, Jing},
booktitle={Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)},
pages={711--719},
year={2021},
organization={SIAM}
}