4 citations · 6 across the 9 of their papers we have counts for
10 papers · 1 filter
A Locally Tokenized Generative Model for Robust Time-Series Watermarking
Dongbin Kim, Geonwoo Shin, Yujin Choi +2
Watermarking is a central tool for provenance in generative models, yet its application to multivariate time series remains hindered by reliability failures under post-editing atta…
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…
BayesNAM: Leveraging Inconsistency for Reliable Explanations
Hoki Kim, Jinseong Park, Yujin Choi +2
Neural additive model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that utilizes neural network-based architectures. Given the advantages of neural…
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…
Fair Sampling in Diffusion Models through Switching Mechanism
Yujin Choi, Jinseong Park, Hoki Kim +2
Diffusion models have shown their effectiveness in generation tasks by well-approximating the underlying probability distribution. However, diffusion models are known to suffer fro…
Differentially Private Sharpness-Aware Training
Jinseong Park, Hoki Kim, Yujin Choi +1
Training deep learning models with differential privacy (DP) results in a degradation of performance. The training dynamics of models with DP show a significant difference from sta…
Improving the Utility of Differentially Private Clustering through Dynamical Processing
Junyoung Byun, Yujin Choi, Jaewook Lee
This study aims to alleviate the trade-off between utility and privacy of differentially private clustering. Existing works focus on simple methods, which show poor performance for…
Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimization
Hoki Kim, Jinseong Park, Yujin Choi +2
Recently, Sharpness-Aware Minimization (SAM) has shown state-of-the-art performance by seeking flat minima. To minimize the maximum loss within a neighborhood in the parameter spac…
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…