Publications

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

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

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Personalized Federated Learning with Gaussian Processes.
NeurIPS, 2021.

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

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Personalized Federated Learning using Hypernetworks.
ICML, 2021.

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

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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]

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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]

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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]

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

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Preprint

Equivariant Architectures for Learning in Deep Weight Spaces.
Preprint, 2023.

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

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Auxiliary Learning as an Asymmetric Bargaining Game.
Preprint, 2023.

Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya
[paper] [project page] [code] [bibtex]

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