6 papers
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
Toan Tran, Waqwoya Abebe, Abhishek Potnis +4
This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can pro…
Differentially Private Synthetic Data via APIs 4: Tabular Data
Toan Tran, Arturs Backurs, Zinan Lin +3
This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive st…
Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents
Toan Tran, Olivera Kotevska, Li Xiong
Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have emerged as an important framew…
Improved Training Technique for Shortcut Models
Anh Nguyen, Viet Nguyen, Duc Vu +4
Shortcut models represent a promising, non-adversarial paradigm for generative modeling, uniquely supporting one-step, few-step, and multi-step sampling from a single trained netwo…
Supercharged One-step Text-to-Image Diffusion Models with Negative Prompts
Viet Nguyen, Anh Nguyen, Trung Dao +4
The escalating demand for real-time image synthesis has driven significant advancements in one-step diffusion models, which inherently offer expedited generation speeds compared to…
Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting
Bao Q. Tran, Viet Nguyen, Anh Tran +1
Diffusion models have demonstrated remarkable capabilities in image synthesis, but their recently proven vulnerability to Membership Inference Attacks (MIAs) poses a critical priva…