TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering. - Seoyoung Hong 001; Minju Jo; Seungji Kook; Jaeeun Jung; Hyowon Wi; Noseong Park; Sung-Bae Cho (IEEE Big Data, 2022)

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 002; Noseong Park (IEEE Big Data, 2023)

RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation. - Jeongwhan Choi 002; Hyowon Wi; Chaejeong Lee; Sung-Bae Cho; Dongha Lee 003; Noseong Park (CoRR, 2023)

An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention. - Yehjin Shin; Jeongwhan Choi 002; Hyowon Wi; Noseong Park (AAAI, 2024)

PANDA: Expanded Width-Aware Message Passing Beyond Rewiring. - Jeongwhan Choi 002; Sumin Park; Hyowon Wi; Sung-Bae Cho; Noseong Park (ICML, 2024)

Polynomial-based Self-Attention for Table Representation Learning. - Jayoung Kim 002; Yehjin Shin; Jeongwhan Choi 002; Hyowon Wi; Noseong Park (ICML, 2024)

Graph Convolutions Enrich the Self-Attention in Transformers! - Jeongwhan Choi 002; Hyowon Wi; Jayoung Kim 002; Yehjin Shin; Kookjin Lee; Nathaniel Trask; Noseong Park (NeurIPS, 2024)

Continuous-time Autoencoders for Regular and Irregular Time Series Imputation. - Hyowon Wi; Yehjin Shin; Noseong Park (WSDM, 2024)

Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering. - Chaejeong Lee; Jeongwhan Choi 002; Hyowon Wi; Sung-Bae Cho; Noseong Park (CoRR, 2024)

SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation. - Chaejeong Lee; Jeongwhan Choi 002; Hyowon Wi; Sung-Bae Cho; Noseong Park (WSDM, 2025)

Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain. - Hyowon Wi; Jeongwhan Choi 002; Noseong Park (CoRR, 2025)