5 papers
A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
Milad Abdollahzadeh, Guimeng Liu, Touba Malekzadeh +3
Generative modeling in machine learning aims to synthesize new data samples that are statistically similar to those observed during training. While conventional generative models s…
IRG: Modular Synthetic Relational Database Generation with Complex Relational Schemas
Jiayu Li, Zilong Zhao, Milad Abdollahzadeh +2
Relational databases (RDBs) are widely used by corporations and governments to store multiple related tables. Their relational schemas pose unique challenges to synthetic data gene…
Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day
Milad Abdollahzadeh, Abdul Raheem, Zilong Zhao +5
Tabular instruction tuning has emerged as a promising research direction for improving LLMs understanding of tabular data. However, the majority of existing works only consider que…
AIR: Zero-shot Generative Model Adaptation with Iterative Refinement
Guimeng Liu, Milad Abdollahzadeh, Ngai-Man Cheung
Zero-shot generative model adaptation (ZSGM) aims to adapt a pre-trained generator to a target domain using only text guidance and without any samples from the target domain. Centr…
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
Christopher T. H Teo, Milad Abdollahzadeh, Xinda Ma +1
Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-to-image (T2I) generation. Specifically, this approach leverages readily available reference imag…