FINE Samples for Learning with Noisy Labels. - Taehyeon Kim 001; Jongwoo Ko; Sangwook Cho; Jinhwan Choi; Se-Young Yun (NeurIPS, 2021)

Self-Contrastive Learning. - Sangmin Bae; Sungnyun Kim; Jongwoo Ko; Gihun Lee; Seungjong Noh; Se-Young Yun (CoRR, 2021)

Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks. - Jaehoon Oh; Jongwoo Ko; Se-Young Yun (EMNLP, 2022)

ALASCA: Rethinking Label Smoothing for Deep Learning Under Label Noise. - Jongwoo Ko; Bongsoo Yi; Se-Young Yun (CoRR, 2022)

Self-Contrastive Learning: Single-Viewed Supervised Contrastive Framework Using Sub-network. - Sangmin Bae; Sungnyun Kim; Jongwoo Ko; Gihun Lee; Seungjong Noh; Se-Young Yun (AAAI, 2023)

A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise. - Jongwoo Ko; Bongsoo Yi; Se-Young Yun (AAAI, 2023)

Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective. - Jongwoo Ko; Seungjoon Park; Minchan Jeong; Sukjin Hong; Euijai Ahn; Du-Seong Chang; Se-Young Yun (EACL (Findings), 2023)

Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding. - Sangmin Bae; Jongwoo Ko; Hwanjun Song; Se-Young Yun (EMNLP, 2023)

NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models. - Jongwoo Ko; Seungjoon Park; Yujin Kim; Sumyeong Ahn; Du-Seong Chang; Euijai Ahn; Se-Young Yun (EMNLP (Findings), 2023)

CUDA: Curriculum of Data Augmentation for Long-tailed Recognition. - Sumyeong Ahn; Jongwoo Ko; Se-Young Yun (ICLR, 2023)

Improving Adaptability and Generalizability of Efficient Transfer Learning for Vision-Language Models. - Yongjin Yang; Jongwoo Ko; Se-Young Yun (CoRR, 2023)

Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models. - Yongjin Yang; Jongwoo Ko; Se-Young Yun (EMNLP, 2024)

DistiLLM: Towards Streamlined Distillation for Large Language Models. - Jongwoo Ko; Sungnyun Kim; Tianyi Chen; Se-Young Yun (ICML, 2024)

Fine-tuning Pre-trained Models for Robustness under Noisy Labels. - Sumyeong Ahn; Sihyeon Kim; Jongwoo Ko; Se-Young Yun (IJCAI, 2024)

HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning. - Tianyi Chen; Xiaoyi Qu; David Aponte; Colby R. Banbury; Jongwoo Ko; Tianyu Ding; Yong Ma; Vladimir Lyapunov; Ilya Zharkov; Luming Liang (CoRR, 2024)

SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins. - Jongwoo Ko; Saket Dingliwal; Bhavana Ganesh; Sailik Sengupta; Sravan Bodapati; Aram Galstyan (CoRR, 2024)

Bayesian Principles Improve Prompt Learning In Vision-Language Models. - Mingyu Kim; Jongwoo Ko; Mijung Park (AISTATS, 2025)

Beyond correlation: The impact of human uncertainty in measuring the effectiveness of automatic evaluation and LLM-as-a-judge. - Aparna Elangovan; Lei Xu 040; Jongwoo Ko; Mahsa Elyasi; Ling Liu; Sravan Babu Bodapati; Dan Roth 001 (ICLR, 2025)

SeRA: Self-Reviewing and Alignment of LLMs using Implicit Reward Margins. - Jongwoo Ko; Saket Dingliwal; Bhavana Ganesh; Sailik Sengupta; Sravan Babu Bodapati; Aram Galstyan (ICLR, 2025)

DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs. - Jongwoo Ko; Tianyi Chen; Sungnyun Kim; Tianyu Ding; Luming Liang; Ilya Zharkov; Se-Young Yun (CoRR, 2025)

When Debate Fails: Bias Reinforcement in Large Language Models. - Jihwan Oh; Minchan Jeong; Jongwoo Ko; Se-Young Yun (CoRR, 2025)

Flex-Judge: Think Once, Judge Anywhere. - Jongwoo Ko; Sungnyun Kim; Sungwoo Cho; Se-Young Yun (CoRR, 2025)

WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference. - Sihan Chen; Dan Zhao; Jongwoo Ko; Colby R. Banbury; Huiping Zhuang; Luming Liang; Tianyi Chen (CoRR, 2025)

Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness. - Yongjin Yang; Euiin Yi; Jongwoo Ko; Kimin Lee; Zhijing Jin 001; Se-Young Yun (CoRR, 2025)