20 citations · 38 across the 11 of their papers we have counts for
6 papers · 1 filter
From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories
Guanglin Zhou, Armin Catic, Motahare Shabestari +4
Access to electronic health records (EHRs) for digital health research is often limited by privacy regulations and institutional barriers. Synthetic EHRs have been proposed as a wa…
Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer
Guanglin Zhou, Sebastiano Barbieri
Generating realistic synthetic electronic health records (EHRs) holds tremendous promise for accelerating healthcare research, facilitating AI model development and enhancing patie…
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Zhongyi Han, Guanglin Zhou, Rundong He +7
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climat…
Emerging Synergies in Causality and Deep Generative Models: A Survey
Guanglin Zhou, Shaoan Xie, Guang-Yuan Hao +7
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have prov…
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects
Guanglin Zhou, Lina Yao, Xiwei Xu +2
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observati…
Cycle-Balanced Representation Learning For Counterfactual Inference
Guanglin Zhou, Lina Yao, Xiwei Xu +2
With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational adve…