1 citations · 1 across the 5 of their papers we have counts for
5 papers
Quality Degradation Attack in Synthetic Data
Qinyi Liu, Dong Liu, Sam Urmian +2
Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the rec…
A Showdown of ChatGPT vs DeepSeek in Solving Programming Tasks
Ronas Shakya, Sam Urmian, Mohammad Khalil
The advancement of large language models (LLMs) has created a competitive landscape for AI-assisted programming tools. This study evaluates two leading models: ChatGPT 03-mini and…
Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
Mohammad Khalil, Sam Urmian, Ronas Shakya +1
In this study, we explore the growing potential of AI and deep learning technologies, particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs), for gene…
Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms
Qinyi Liu, Oscar Deho, Sam Urmian +3
The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purp…
From Width-Based Model Checking to Width-Based Automated Theorem Proving
Mateus de Oliveira Oliveira, Sam Urmian
In the field of parameterized complexity theory, the study of graph width measures has been intimately connected with the development of width-based model checking algorithms for c…