3 citations · 8 across the 9 of their papers we have counts for
12 papers
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…
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…
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…
Are Self-Attentions Effective for Time Series Forecasting?
Dongbin Kim, Jinseong Park, Jaewook Lee +1
Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically advanced the landscape of forecasti…
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…
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…
Comment on Transferability and Input Transformation with Additive Noise
Hoki Kim, Jinseong Park, Jaewook Lee
Adversarial attacks have verified the existence of the vulnerability of neural networks. By adding small perturbations to a benign example, adversarial attacks successfully generat…
Bridged Adversarial Training
Hoki Kim, Woojin Lee, Sungyoon Lee +1
Adversarial robustness is considered as a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different…
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
Sungyoon Lee, Hoki Kim, Jaewook Lee
Deep learning is vulnerable to adversarial examples. Many defenses based on randomized neural networks have been proposed to solve the problem, but fail to achieve robustness again…