2 citations · 7 across the 6 of their papers we have counts for
6 papers
HiGen: Hierarchical Graph Generative Networks
Mahdi Karami
Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. To address this limitation, we propose a novel graph genera…
On Hierarchical Multi-Resolution Graph Generative Models
Mahdi Karami, Jun Luo
In real world domains, most graphs naturally exhibit a hierarchical structure. However, data-driven graph generation is yet to effectively capture such structures. To address this,…
DP-VAE: Differentially Private Pre-trained Variational Autoencoders
Dihong Jiang, Guojun Zhang, Mahdi Karami +3
Modern machine learning systems achieve great success when trained on large datasets. However, these datasets usually contain sensitive information (e.g. medical records, face imag…
Robust One Round Federated Learning with Predictive Space Bayesian Inference
Mohsin Hasan, Zehao Zhang, Kaiyang Guo +4
Making predictions robust is an important challenge. A separate challenge in federated learning (FL) is to reduce the number of communication rounds, particularly since doing so re…
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
Haolin Yu, Kaiyang Guo, Mahdi Karami +3
In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models tha…
Variational Inference for Deep Probabilistic Canonical Correlation Analysis
Mahdi Karami, Dale Schuurmans
In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) descriptio…