20 citations · 38 across the 11 of their papers we have counts for
11 papers
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…
Retrieval-Augmented Review Generation for Poisoning Recommender Systems
Shiyi Yang, Xinshu Li, Guanglin Zhou +4
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks, where malicious actors inject fake user profiles, including a group of wel…
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…
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions. Despite this, domai…
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining…
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…
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Guanglin Zhou, Chengkai Huang, Xiaocong Chen +4
The field of generating recommendations within the framework of causal inference has seen a recent surge, with recommendations being likened to treatments. This approach enhances i…
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…
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
Xiaocong Chen, Lina Yao, Julian McAuley +2
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely an…