34 citations · 59 across the 3 of their papers we have counts for
14 papers
Personalized Federated Learning with Gaussian Processes
Idan Achituve, Aviv Shamsian, Aviv Navon +2
Federated learning aims to learn a global model that performs well on client devices with limited cross-client communication. Personalized federated learning (PFL) further extends…
Personalized Federated Learning using Hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya +1
Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models…
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini +2
Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially co…
Learning the Pareto Front with Hypernetworks
Aviv Navon, Aviv Shamsian, Gal Chechik +1
Multi-objective optimization (MOO) problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the fron…
On Learning Sets of Symmetric Elements
Haggai Maron, Or Litany, Gal Chechik +1
Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are…
Understanding the Limitations of Conditional Generative Models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl +1
Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a rob…