no code implementations • 27 Jul 2021 • Farwa K. Khan, Adrian Flanagan, Kuan E. Tan, Zareen Alamgir, Muhammad Ammad-Ud-Din
We introduce the payload optimization method for federated recommender systems (FRS).
no code implementations • 8 Apr 2020 • Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-Ud-Din
We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources.
1 code implementation • 29 Jan 2019 • Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, Adrian Flanagan
In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates.
no code implementations • 7 Dec 2016 • Luana Micallef, Iiris Sundin, Pekka Marttinen, Muhammad Ammad-Ud-Din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski
The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.
no code implementations • 11 Jun 2016 • Muhammad Ammad-Ud-Din, Suleiman A. Khan, Disha Malani, Astrid Murumägi, Olli Kallioniemi, Tero Aittokallio, Samuel Kaski
We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors.
no code implementations • 20 May 2016 • Marta Soare, Muhammad Ammad-Ud-Din, Samuel Kaski
We consider regression under the "extremely small $n$ large $p$" condition, where the number of samples $n$ is so small compared to the dimensionality $p$ that predictors cannot be estimated without prior knowledge.