Improving Open Directory Project-Based Text Classification with Hierarchical Category Embedding. - Ji-Min Lee; Kang-Min Kim; Yeachan Kim; SangKeun Lee 001 (ICCI*CC, 2018)

A Unified Approach to Word Sense Representation and Disambiguation. - Do-Myoung Lee; Yeachan Kim; Ji-Min Lee; SangKeun Lee 001 (ICCI*CC, 2018)

Learning to Generate Word Representations using Subword Information. - Yeachan Kim; Kang-Min Kim; Ji-Min Lee; SangKeun Lee 001 (COLING, 2018)

From Small-scale to Large-scale Text Classification. - Kang-Min Kim; Yeachan Kim; Jungho Lee; Ji-Min Lee; SangKeun Lee 001 (WWW, 2019)

Adaptive Compression of Word Embeddings. - Yeachan Kim; Kang-Min Kim; SangKeun Lee 001 (ACL, 2020)

Multi-pretraining for Large-scale Text Classification. - Kang-Min Kim; Bumsu Hyeon; Yeachan Kim; Jun-Hyung Park; SangKeun Lee 001 (EMNLP (Findings), 2020)

Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks. - Yeachan Kim; Kang-Min Kim; SangKeun Lee 001 (LREC, 2020)

Personalizing large-scale text classification by modeling individual differences. - Jungho Lee; Byung-Ju Choi; Yeachan Kim; Kang-Min Kim; Woo-Jong Ryu; SangKeun Lee 001 (SAC, 2020)

An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention. - Yeachan Kim; Bonggun Shin (MLHC, 2021)

In Defense of Core-set: A Density-aware Core-set Selection for Active Learning. - Yeachan Kim; Bonggun Shin (KDD, 2022)

Context-based Virtual Adversarial Training for Text Classification with Noisy Labels. - Do-Myoung Lee; Yeachan Kim; Chang-gyun Seo (LREC, 2022)

Dynamic Structure Pruning for Compressing CNNs. - Jun-Hyung Park; Yeachan Kim; Junho Kim; Joon-Young Choi; SangKeun Lee 001 (AAAI, 2023)

Client-Customized Adaptation for Parameter-Efficient Federated Learning. - Yeachan Kim; Junho Kim; Wing-Lam Mok; Jun-Hyung Park; SangKeun Lee 001 (ACL (Findings), 2023)

Improving Bias Mitigation through Bias Experts in Natural Language Understanding. - Eojin Jeon; Mingyu Lee; Juhyeong Park; Yeachan Kim; Wing-Lam Mok; SangKeun Lee 001 (EMNLP, 2023)

Leap-of-Thought: Accelerating Transformers via Dynamic Token Routing. - Yeachan Kim; Junho Kim; Jun-Hyung Park; Mingyu Lee; SangKeun Lee 001 (EMNLP, 2023)

Phase-shifted adversarial training. - Yeachan Kim; Seongyeon Kim; Ihyeok Seo; Bonggun Shin (UAI, 2023)

Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization. - Yeachan Kim; Bonggun Shin (CoRR, 2023)

SparseFlow: Accelerating Transformers by Sparsifying Information Flows. - Yeachan Kim; SangKeun Lee 001 (ACL (1), 2024)

Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning. - Yeachan Kim; Junho Kim; SangKeun Lee 001 (ACL (1), 2024)

KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters. - SungHo Kim; Juhyeong Park; Yeachan Kim; SangKeun Lee 001 (ACL (Findings), 2024)

MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science. - Junho Kim; Yeachan Kim; Jun-Hyung Park; Yerim Oh; Suho Kim; SangKeun Lee 001 (EMNLP (Findings), 2024)

SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Science. - Yeachan Kim; Jun-Hyung Park; SungHo Kim; Juhyeong Park; Sangyun Kim; SangKeun Lee 001 (EMNLP (Industry Track), 2024)

MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction. - Jun-Hyung Park; Yeachan Kim; Mingyu Lee; Hyuntae Park; SangKeun Lee 001 (EMNLP, 2024)

Zero-shot Commonsense Reasoning over Machine Imagination. - Hyuntae Park; Yeachan Kim; Jun-Hyung Park; SangKeun Lee 001 (EMNLP (Findings), 2024)

Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction. - Jun-Hyung Park; Hyuntae Park; Yeachan Kim; Woosang Lim; SangKeun Lee 001 (EMNLP (Industry Track), 2024)

Dynamic Layer-Wise Token Pruning for Sequence-to-Sequence Transformer Inference. - Ji Hun Keom; Yeachan Kim; Sung Ju Lee; SangKeun Lee 001 (ICPRAI (2), 2024)

C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning. - Yeachan Kim; Junho Kim; Wing-Lam Mok; Jun-Hyung Park; SangKeun Lee 001 (CoRR, 2024)

CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning. - Yeachan Kim; Junho Kim; SangKeun Lee 001 (CoRR, 2024)

Forward Knows Efficient Backward Path: Saliency-Guided Memory-Efficient Fine-tuning of Large Language Models. - Yeachan Kim; SangKeun Lee 001 (ACL (1), 2025)

Curriculum Debiasing: Toward Robust Parameter-Efficient Fine-Tuning Against Dataset Biases. - Mingyu Lee; Yeachan Kim; Wing-Lam Mok; SangKeun Lee 001 (ACL (1), 2025)