1 citations · 1 across the 8 of their papers we have counts for
9 papers
Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood
Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann +4
Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training data. We introduce MissCNF, whic…
Source Distribution Estimation by Posterior Averaging
Trung-Dung Hoang, Lisa M. Koch
Simulation-based science often requires a distribution over simulator parameters whose push-forward reproduces a set of real observations: this is the source distribution estimatio…
Turning Threat into Opportunity: DRL-Powered Anti-Jamming via Energy Harvesting in UAV-Disrupted Channels
Ngoc-Tan Nguyen, Thi-Thu Hoang, Trung-Dung Hoang +1
The open and broadcast nature of wireless communication systems, while enabling ubiquitous connectivity, also exposes them to jamming attacks that may critically compromise network…
Label-free estimation of clinically relevant performance metrics under distribution shifts
Tim Flühmann, Alceu Bissoto, Trung-Dung Hoang +1
Performance monitoring is essential for safe clinical deployment of image classification models. However, because ground-truth labels are typically unavailable in the target datase…
A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference
Trung-Dung Hoang, Alceu Bissoto, Vihangkumar V. Naik +4
Accurately estimating parameters of physiological models is essential to achieving reliable digital twins. For Type 1 Diabetes, this is particularly challenging due to the complexi…
Subgroup Performance Analysis in Hidden Stratifications
Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann +3
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level…
Evaluating Precise Geolocation Inference Capabilities of Vision Language Models
Neel Jay, Hieu Minh Nguyen, Trung Dung Hoang +1
The prevalence of Vision-Language Models (VLMs) raises important questions about privacy in an era where visual information is increasingly available. While foundation VLMs demonst…
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion
Vinh Tong, Hoang Trung-Dung, Anji Liu +2
In domains such as molecular and protein generation, physical systems exhibit inherent symmetries that are critical to model. Two main strategies have emerged for learning invarian…
Learning to Discretize Denoising Diffusion ODEs
Vinh Tong, Hoang Trung-Dung, Anji Liu +2
Diffusion Probabilistic Models (DPMs) are generative models showing competitive performance in various domains, including image synthesis and 3D point cloud generation. Sampling fr…