Preprints:

• A Study on the Evaluation of Generative Models, Eyal Betzalel, Coby Penso, Aviv Navon, and Ethan Fetaya.
• Communication Efficient Distributed Learning over Wireless Channels, Idan Achituve, Wenbo Wang, Ethan Fetaya, Amir Leshem.
• Equivariant Architectures for Learning in Deep Weight Spaces, Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron.
• Auxiliary Learning as an Asymmetric Bargaining Game, Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya.
• Guided Deep Kernel Learning, Idan Achituve, Gal Chechik, Ethan Fetaya.

Publications:

• Geometric deep optical sensing, Shaofan Yuan,ChaoMa, Ethan Fetaya, Thomas Mueller, Doron Naveh, Fan Zhang, and Fengnian Xia. Science 2023.
• Functional Ensemble Distillation, Coby Penso, Idan Achituve, and Ethan Fetaya. NeurIPS 2022.
• Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?, Guy Heller and Ethan Fetaya. SaTML 2022.
• Evaluating and Calibrating Uncertainty Prediction in Regression Tasks, Dan Levi, Liran Gispan, Niv Giladi and Ethan Fetaya. Sensors 2022.
• Multi-task Learning as a Bargaining Game, Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya. ICML 2022.
• Personalized Federated Learning with Gaussian Processes, Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, and Ethan Fetaya. NeurIPS 2021.
• From local structures to size generalization in graph neural networks, Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron. ICML 2021.
• Personalized federated learning using hypernetworks, Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik. ICML 2021.
• GP-Tree: A Gaussian process classifier for few-shot incremental learning, Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, and Ethan Fetaya. ICML 2021.
• Scene-Agnostic Multi-Microphone Speech Dereverberation, Yochai Yemini, Ethan Fetaya, Haggai Maron, and Sharon Gannot. INTERSPEECH 2021.
• Learning the Pareto Front with Hypernetworks, Aviv Navon*, Aviv Shamsian*, Gal Chechik, Ethan Fetaya (*equal contribution) [code]. ICLR 2021.
• Auxiliary Learning by Implicit Differentiation, Aviv Navon*, Idan Achituve∗, Haggai Maron, Gal Chechik†, Ethan Fetaya† (*,† equal contribution) [code]. ICLR 2021
• Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks, Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, and Shai Gordin. PNAS 2020.
• On Learning Sets of Symmetric Elements, Haggai Maron, Or Litany, Gal Chechik and  Ethan Fetaya, ICML 2020 (Best paper award).
• Understanding the Limitations of Conditional Generative Models, Ethan Fetaya*, Joern-Henrik Jacobsen(, Will Grathwohl, Richard Zemel (*equal contribution) ICLR 2020
• Incremental few-shot learning with attention attractor networks, Mengye Ren, Renjie Liao, Ethan Fetaya and Richard Zemel. Neurips 2019.
• On the Universality of Invariant Networks, Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman. ICML 2019
• Neural guided constraint logic programming for program synthesis,Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun and Richard Zemel. Neurips 2018.
• Reviving and improving recurrent back-propagation, Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, and Richard Zemel. ICML 2018.
• Neural relational inference for interacting systems, Thomas Kipf*, Ethan Fetaya*, Kuan-Chieh Wang, Max Welling, Richard Zemel (*equal contribution). ICML 2018.
• Learning discrete weights using the local reparameterization trick, Oran Shayer, Dan Levi and Ethan Fetaya. ICLR 2018.
• Human pose estimation using deep consensus voting, Ita Lifshitz*, Ethan Fetaya* and Shimon Ullman (*equal contribution). ECCV 2016.
•  Unsupervised ensemble learning with dependent classifiers, Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang and Yuval Kluger. AISTATS 2016.
• Atoms of recognition in human and computer vision, Shimon Ullman, Liav Assif, Ethan Fetaya and Daniel Harari. PNAS 2016.
• StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. Dan Levi, Noa Garnett and Ethan Fetaya. BMVC 2016.
• Learning local invariant Mahalanobis distances. Ethan Fetaya and Shimon Ullman. ICML 2015.
• Graph approximation and clustering on a budget. Ethan Fetaya, Ohad Shamir, Shimon Ullman. AISTATS 2015.
• Bounding the distance of quantum surface codes. Ethan Fetaya, Journal of Mathematical Physics, 2012.