216 citations · 399 across the 11 of their papers we have counts for
12 papers
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation
Muhammad Irfan Khan, Esa Alhoniemi, Elina Kontio +2
Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client d…
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation
Muhammad Irfan Khan, Mohammad Ayyaz Azeem, Esa Alhoniemi +3
In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage an…
A generalized deep learning model for multi-disease Chest X-Ray diagnostics
Nabit Bajwa, Kedar Bajwa, Atif Rana +3
We investigate the generalizability of deep convolutional neural network (CNN) on the task of disease classification from chest x-rays collected over multiple sites. We systematica…
Federated Multi-view Matrix Factorization for Personalized Recommendations
Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy +3
We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources. Our method is ab…
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan +4
The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is dist…
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
Muhammad Ammad-ud-din, Suleiman A. Khan, Disha Malani +4
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sam…
Convex Factorization Machine for Regression
Makoto Yamada, Wenzhao Lian, Amit Goyal +6
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs). Specifically, we employ a linear+quadratic model and r…
Bayesian multi-tensor factorization
Suleiman A. Khan, Eemeli Leppäaho, Samuel Kaski
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem gene…
Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis
Suleiman A Khan, Seppo Virtanen, Olli P Kallioniemi +3
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how…
Kernelized Bayesian Matrix Factorization
Mehmet Gönen, Suleiman A. Khan, Samuel Kaski
We extend kernelized matrix factorization with a fully Bayesian treatment and with an ability to work with multiple side information sources expressed as different kernels. Kernel…
Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs
Suleiman A. Khan, Ali Faisal, John Patric Mpindi +6
Detailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As most compounds impact multiple tar…
Bayesian Group Factor Analysis
Seppo Virtanen, Arto Klami, Suleiman A. Khan +1
We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor anal…