Multi-Task Learning as a Bargaining Game.
ICML, 2022.

Aviv Shamsian, Idan Achituve, Aviv Navon, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya
[paper] [project page] [video] [poster] [code] [bibtex]

Personalized Federated Learning with Gaussian Processes.
NeurIPS, 2021.

Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya
[paper] [code]

Personalized Federated Learning using Hypernetworks.
ICML, 2021.

Aviv Shamsian*, Aviv Navon*, Ethan Fetaya, Gal Chechik
(* equal contribution)
[paper] [project page] [code] [bibtex]

GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning.
ICML, 2021.

Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya
[paper] [bibtex]

Learning the Pareto Front with Hypernetworks.
ICLR, 2021.
Aviv Navon*, Aviv Shamsian*, Gal Chechik, Ethan Fetaya
(* equal contribution)
[paper] [project page] [video] [poster] [code] [bibtex]

Auxiliary Learning by Implicit Differentiation.
ICLR, 2021.
Aviv Navon*, Idan Achituve*, Haggai Maron, Gal Chechik†, Ethan Fetaya†
(*,† equal contribution)
[paper] [project page] [video] [poster] [code] [bibtex]

Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models.
Electronic Journal of Statistics, 2020.
Aviv Navon, Saharon Rosset
[paper] [code] [bibtex]


A Study on the Evaluation of Generative Models.
Preprint, 2022.
Eyal Betzalel, Coby Penso, Aviv Navon, Ethan Fetaya