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- Fei WangSuggested from co-authorship
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Works18 from public data
- MetaPred124
In order to predict the target risk with limited data samples, a meta-learner is trained from a set of related risk prediction tasks which learns how a good predictor is trained, which can be directly used in target risk prediction, and the limited available samples in the target domain can be used for further fine-tuning the model performance.
- Differentially Private Federated Learning with Local Regularization and Sparsification120
This paper finds the key to solving the cause of model performance degradation in federated learning with user-level DP guarantee is to naturally restrict the norm of local updates before ex-ecuting operations that guarantee DP, and proposes two techniques to increase model quality without sacrificing privacy.
- Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks69
This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis using a real world critical care EHR corpus including 37,486 ICU stays.
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- Maximum Entropy Reinforcement Learning with Diffusion Policy41
The diffusion model is employed, a powerful generative model capable of capturing complex multimodal distributions, as the policy representation to fulfill the MaxEnt RL objective, developing a method named MaxEnt RL with Diffusion Policy (MaxEntDP), which enables efficient exploration and brings the policy closer to the optimal MaxEnt policy.
- DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy39
It is demonstrated that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies, and the very first framework that employs neural architecture search to automatic model design for privatedeep learning, dubbed as DPNAS is proposed.
- APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers35
This paper proposes APRIL - Attention PRIvacy Leakage, which poses a strong threat to self-attention inspired models such as ViT, and urges the significance of designing privacy-safer Transformer models and defending schemes.
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- GraphLeak: Patient Record Leakage through Gradients with Knowledge Graph5
This paper presents GraphLeak, which incorporates the medical knowledge graph in gradient leakage attacks, and has achieved state-of-the-art attack performance compared with previous works.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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