4 citations · 5 across the 10 of their papers we have counts for
5 papers · 1 filter
MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data
Masoumeh Shafieinejad, Xi He, Mahshid Alinoori +6
Synthetic data is often perceived as a silver-bullet solution to data anonymization and privacy-preserving data publishing. Drawn from generative models like diffusion models, synt…
Adaptive Latent-Space Constraints in Personalized Federated Learning
Sana Ayromlou, Fatemeh Tavakoli, D. B. Emerson
Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both secur…
Federated Impression for Learning with Distributed Heterogeneous Data
Atrin Arya, Sana Ayromlou, Armin Saadat +2
Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Fed…
CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning
Sana Ayromlou, Teresa Tsang, Purang Abolmaesumi +1
In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from al…
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets
Fatemeh Tavakoli, D. B. Emerson, Sana Ayromlou +5
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning…