REAL @ KENTECH - Publications

[C] Conference, [J] Journal, [W] Workshopt, [P] Preprint, [*] Equal contribution, [^] Equal correspondence

2024

[P6]ConDS: Context Distribution Shift for Robust In-Context Learning

[P5]Distribured In-Context Learning Under Non-IID Among Clients

[P4]Prompt Learning with Noisy Labels

[P3]ORBIS: Open Dataset Can Rescue You From Dataset Bias Problem

[P2]Large Language Models In Medical Term Classification And Unexpected Misalignment Between Response and Reasoning

Xiaodan Zhang, Sandeep Vemulapalli, Nabasmita Talukdar, Sumyeong Ahn, Jiankun Wang, Han Meng, Sardar Mehtab Bin Murtaza, Aakash Ajay Dave, Dmitry Leshchiner, Dimitri F Joseph, Martin Witteveen-Lane, Dave Chesla, Jiayu Zhou, Bin Chen
Preprint

[P1]Augmented Risk Prediction for Onset of Alzheimer’s Disease from Electronic Health Records with Large Language Models

Jiankun Wang, Sumyeong Ahn, Taykhoom Dalal, Xiaodan Zhang, Weishen Pan, Qiannan Zhang, Bin Chen, Hiroko H Dodge, Fei Wang, Jiayu Zhou
Preprint

[W7] VaCoDe: Visual Augmented Contrastive Learning (📄)

[W6] FedDr+: Stabilizing Dot-Regression with Global Feature Distillation for Federated Learning (📄)

Seongyoon Kim, Minchan Jeong, Sungnyun Kim, Sungwoo Cho, Sumyeong Ahn^, Se-Young Yun^
KDD-W 2024

[C11] Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes (📄)

Xiaodan Zhang, Nabasmita Talukdar, Sandeep Vemulapalli, Sumyeong Ahn, Jiankun Wang, Han Meng, Sardar Mehtab Bin Murtaza, Dmitry Leshchiner, Aakash Ajay Dave, Dimitri F Joseph, Martin Witteveen-Lane, Dave Chesla, Jiayu Zhou, Bin Chen
AMIA 2024

[C10] Fine-Tuning Pre-trained Models for Robustness Under Noisy Labels (📄)

[C9] Active Prompt Learning in Vision Language Models (📄)

2023

[C8] NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models (📄)

Jongwoo Ko*, Seungjoon Park*, Yujin Kim, Du-Seong Chang, Euijai Ahn, Sumyeong Ahn^, Se-Young Yun^
EMNLP 2023 (Findings)

[C7] Denoising After Entropy-based Debiasing: A Robust Training Method for Dataset Bias with Noisy Labels (📄)

[C6/W3] CUDA: Curriculum of Data Augmentation for Long-tailed Recognition (📄)

[C5/W2] Mitigating Dataset Bias By Using Per-sample Gradient (📄)

[W1] Efficient Utilization of Pre-trained Model for Learning with Noisy Labels (📄)

Before 2022

[C4] Client Sampling Algorithm in Federated Learning via Combinatorial Averaging and Multi-armed Bandits (📄)

[C3] Neuro-DCF: Design of Wireless MAC via Multi-Agent Reinforcement Learning Approach (📄)

[C2] Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation (📄)

[C1] Multi-armed Bandit with Additional Observations (📄)