632 citations · 2.1k across the 24 of their papers we have counts for
5 papers · 1 filter
A Novel Federated Learning Framework for Defending Against Gradient Inversion Attacks
Pengxin Guo, Ziying Huang, Shuang Zeng +4
Federated Learning (FL) offers a paradigm for collaborative machine learning model training among clients, preserving data privacy by avoiding direct data sharing. However, the gra…
CircuitGate: Logic-Consistent Circuit-Level Functional Modeling for And-Inverter Graphs
Qifan Zhang, Ruijie Li, Fangzhou Zhang +6
And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA). As structured representations of complex digi…
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu +4
Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left un…
High-dimensional Many-to-many-to-many Mediation Analysis
Tien Dat Nguyen, Trung Khang Tran, Cong Khanh Truong +3
We study high-dimensional mediation analysis in which exposures, mediators, and outcomes are all multivariate, and both exposures and mediators may be high-dimensional. We formaliz…
Generative Model via Quantile Assignment
Georgi Hrusanov, Oliver Y. Chén, Julien S. Bodelet
Deep Generative models (DGMs) play two key roles in modern machine learning: (i) producing new information (e.g., image synthesis) and (ii) reducing dimensionality. However, tradit…