2 citations · 2 across the 2 of their papers we have counts for
9 papers
Leveraging Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosis
Woojin Jeong, Yujin Choi, Dongbin Kim +2
Medical imaging models often degrade when deployed at new clinical sites due to differences in imaging equipment, protocols, and patient populations. Test-time adaptation (TTA) add…
Stability Analysis of Sharpness-Aware Minimization
Hoki Kim, Jinseong Park, Yujin Choi +1
Sharpness-aware minimization (SAM) is a training method that seeks to find flat minima in deep learning, resulting in state-of-the-art performance across various domains. Instead o…
TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation
Jinseong Park, Seungyun Lee, Woojin Jeong +2
Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis testing. Recently, diffusion models have emerged as the de fa…
Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home?
Yujin Choi, Youngjoo Park, Junyoung Byun +2
Retrieval-augmented generation (RAG) mitigates the hallucination problem in large language models (LLMs) and has proven effective for personalized usages. However, delivering priva…
Multi-Class Support Vector Machine with Differential Privacy
Jinseong Park, Yujin Choi, Jaewook Lee
With the increasing need to safeguard data privacy in machine learning models, differential privacy (DP) is one of the major frameworks to build privacy-preserving models. Support…
Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training
Yujin Choi, Jinseong Park, Junyoung Byun +1
Programmatically generated synthetic data has been used in differential private training for classification to enhance performance without privacy leakage. However, as the syntheti…