Publications by Category

Arxiv

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

👥 Hyunji Jung*, Hanseul Cho*, and Chulhee Yun

📰 ICLR 2025 🗓 January 23, 2025 🔗 [paper] [arxiv]

🎉 Best Paper Award at the 11th Joint Conference of Korean Artificial Intelligence Association (JKAIA 2024)

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

👥 Hanseul Cho*, Jaeyoung Cha*, Srinadh Bhojanapalli, and Chulhee Yun

📰 ICLR 2025 🗓 January 23, 2025 🔗 [paper] [arxiv] [code]

ICLR

SGDA with Shuffling: Faster Convergence For Nonconvex-PŁ Minimax Optimization

👥 Hanseul Cho and Chulhee Yun

📰 ICLR 2023 🗓 February 02, 2023 🔗 [paper] [arxiv]

🎉 NAVER Outstanding Theory Paper Award at the 7th Joint Conference of Korea Artificial Intelligence Association (JKAIA 2022)

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

👥 Hyunji Jung*, Hanseul Cho*, and Chulhee Yun

📰 ICLR 2025 🗓 January 23, 2025 🔗 [paper] [arxiv]

🎉 Best Paper Award at the 11th Joint Conference of Korean Artificial Intelligence Association (JKAIA 2024)

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

👥 Hanseul Cho*, Jaeyoung Cha*, Srinadh Bhojanapalli, and Chulhee Yun

📰 ICLR 2025 🗓 January 23, 2025 🔗 [paper] [arxiv] [code]

ICLR Workshop

Fundamental Benefit of Alternating Updates in Minimax Optimization

👥 Jaewook Lee*, Hanseul Cho*, and Chulhee Yun

📰 ICML 2024 (Short version at ICLR 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning (BGPT)) 🗓 May 02, 2024 🔗 [paper] [arxiv] [code]

🎉 Spotlight @ ICML 2024 (Top 3.5% among total submissions)

ICML

Fundamental Benefit of Alternating Updates in Minimax Optimization

👥 Jaewook Lee*, Hanseul Cho*, and Chulhee Yun

📰 ICML 2024 (Short version at ICLR 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning (BGPT)) 🗓 May 02, 2024 🔗 [paper] [arxiv] [code]

🎉 Spotlight @ ICML 2024 (Top 3.5% among total submissions)

ICML Workshop

DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity

👥 Baekrok Shin*, Junsoo Oh*, Hanseul Cho, and Chulhee Yun

📰 NeurIPS 2024 (Short version at ICML 2024 Workshop on Advancing Neural Network Training (WANT)) 🗓 September 26, 2024 🔗 [paper] [arxiv] [code]

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure

👥 Hanseul Cho*, Jaeyoung Cha*, Pranjal Awasthi, Srinadh Bhojanapalli, Anupam Gupta, and Chulhee Yun

📰 NeurIPS 2024 (Short version at ICML 2024 Workshop on Long-Context Foundation Models (LCFM)) 🗓 September 26, 2024 🔗 [paper] [arxiv] [code]

KAIA

SGDA with Shuffling: Faster Convergence For Nonconvex-PŁ Minimax Optimization

👥 Hanseul Cho and Chulhee Yun

📰 ICLR 2023 🗓 February 02, 2023 🔗 [paper] [arxiv]

🎉 NAVER Outstanding Theory Paper Award at the 7th Joint Conference of Korea Artificial Intelligence Association (JKAIA 2022)

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

👥 Hyunji Jung*, Hanseul Cho*, and Chulhee Yun

📰 ICLR 2025 🗓 January 23, 2025 🔗 [paper] [arxiv]

🎉 Best Paper Award at the 11th Joint Conference of Korean Artificial Intelligence Association (JKAIA 2024)

Hanseul Cho

Publications by Category

Arxiv

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

ICLR

SGDA with Shuffling: Faster Convergence For Nonconvex-PŁ Minimax Optimization

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

ICLR Workshop

Fundamental Benefit of Alternating Updates in Minimax Optimization

ICML

Fundamental Benefit of Alternating Updates in Minimax Optimization

ICML Workshop

DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure

KAIA

SGDA with Shuffling: Faster Convergence For Nonconvex-PŁ Minimax Optimization

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

NeurIPS

PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning

Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint

DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity

Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure