40 citations · 96 across the 15 of their papers we have counts for
6 papers · 1 filter
Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data
Zilong Zhao, Abdul Raheem, Jiayu Li +5
Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, t…
Label-Only Model Inversion Attacks via Knowledge Transfer
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML) model to infer and reconstruct private training data. Remarkable progress has been made in t…
On Measuring Fairness in Generative Models
Christopher T. H. Teo, Milad Abdollahzadeh, Ngai-Man Cheung
Recently, there has been increased interest in fair generative models. In this work, we conduct, for the first time, an in-depth study on fairness measurement, a critical component…
Re-thinking Model Inversion Attacks Against Deep Neural Networks
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive informa…
Fair Generative Models via Transfer Learning
Christopher TH Teo, Milad Abdollahzadeh, Ngai-Man Cheung
This work addresses fair generative models. Dataset biases have been a major cause of unfairness in deep generative models. Previous work had proposed to augment large, biased data…
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man Cheung
Multimodal meta-learning is a recent problem that extends conventional few-shot meta-learning by generalizing its setup to diverse multimodal task distributions. This setup makes a…