SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization. - Hanseul Cho 002; Chulhee Yun (ICLR, 2023)

PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning. - Hojoon Lee; Hanseul Cho 002; Hyunseung Kim; Daehoon Gwak; Joonkee Kim; Jaegul Choo; Se-Young Yun; Chulhee Yun (NeurIPS, 2023)

Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint. - Junghyun Lee; Hanseul Cho 002; Se-Young Yun; Chulhee Yun (NeurIPS, 2023)

Enhancing Generalization and Plasticity for Sample Efficient Reinforcement Learning. - Hojoon Lee; Hanseul Cho 002; Hyunseung Kim; Daehoon Gwak; Joonkee Kim; Jaegul Choo; Se-Young Yun; Chulhee Yun (CoRR, 2023)

Fundamental Benefit of Alternating Updates in Minimax Optimization. - Jaewook Lee 009; Hanseul Cho 002; Chulhee Yun (ICML, 2024)

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure. - Hanseul Cho 002; Jaeyoung Cha; Pranjal Awasthi; Srinadh Bhojanapalli; Anupam Gupta 001; Chulhee Yun (NeurIPS, 2024)

DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity. - Baekrok Shin; Junsoo Oh; Hanseul Cho 002; Chulhee Yun (NeurIPS, 2024)

Position Coupling: Leveraging Task Structure for Improved Length Generalization of Transformers. - Hanseul Cho 002; Jaeyoung Cha; Pranjal Awasthi; Srinadh Bhojanapalli; Anupam Gupta 001; Chulhee Yun (CoRR, 2024)

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count. - Hanseul Cho 002; Jaeyoung Cha; Srinadh Bhojanapalli; Chulhee Yun (ICLR, 2025)

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification. - Hyunji Jung; Hanseul Cho 002; Chulhee Yun (ICLR, 2025)

The Coverage Principle: A Framework for Understanding Compositional Generalization. - Hoyeon Chang; Jinho Park 005; Hanseul Cho 002; Sohee Yang; Miyoung Ko; Hyeonbin Hwang; Seungpil Won; Dohaeng Lee; Youbin Ahn; Minjoon Seo (CoRR, 2025)