Aviv Netanyahu

I am a PhD candidate in EECS at MIT CSAIL, advised by Prof. Pulkit Agrawal, as part of the Embodied Intelligence Community of Research.

I earned my MSc from the Faculty of Mathematics and Computer Science at the Weizmann Institute of Science, advised by Prof. Shimon Ullman, where I worked on Computer Vision. Before that, I received my BSc in Mathematics and Computer Science from the Hebrew University of Jerusalem.

Email / Twitter / LinkedIn

Research

I am interested in studying how to deploy agents in new environments with minimum to no supervision. My research focuses, in this context, on inferring how agents interact with the environment and with other agents in ways that are generalizable to new settings. Issues related to this research include combinatorial and out-of-distribution generalization, learning from demonstrations, human in the loop learning and object-centric learning.

	Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-time Policy Adaptation Andi Peng, Aviv Netanyahu, Mark Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal project page / arXiv / video / MIT News ICML 2023 NeurIPS Workshop on Human in the Loop Learning, 2022 Counterfactual demonstrations for personalized policy adaptation.
	Learning to Extrapolate: A Transductive Approach Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal arXiv / video / code / MIT lab tours ICLR 2023 NeurIPS Workshop on Distribution Shifts, 2022 Out-of-support generalization via a transductive reparameterization.
	Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning Aviv Netanyahu, Tianmin Shu, Joshua B. Tenenbaum, Pulkit Agrawal project page / arXiv / talk / code ICML 2022 RLDM 2022 ICLR Elements of Reasoning: Objects, Structure and Causality Workshop, 2022 Graph-based reward learning via active learning for object rearrangement.
	PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, Joshua B. Tenenbaum project page / arXiv / talk / code AAAI 2021 NeurIPS Shared Visual Representations in Human & Machine Intelligence Workshop, 2020 (Oral Presentation, Best Paper Award) A dataset for inferring physically grounded social interactions between agents.
	Image Interpretation by Iterative Bottom-Up Top-Down Processing Shimon Ullman, Liav Assif, Alona Strugatski, Ben-Zion Vatashsky, Hila Levi, Aviv Netanyahu, Adam Yaari arXiv / code under review Extraction and representation of scene components and the relations between them by combining bottom-up and top-down networks, interacting through a symmetric bi-directional communication.
	Cyclical Bottom-Up Top-Down Neural Networks for Relational Reasoning MSc thesis A system for selective visual relationship detection that achieves out-of-distribution and combinatorial generalization.

Teaching

TA, Machine Learning (6.867), MIT Fall 2021
Co-instructor, Deep Learning for Control (6.S090), MIT Winter 2021
Project Mentor, Computational Cognitive Science (9.66), MIT Fall 2020
Grader, Logic and Set Theory (69), Reichman University Spring 2017
Grader and Mathematics Help Center Instructor, Linear Algebra 2 (80135), Hebrew University of Jerusalem Spring 2016

Service

Reviewer NeurIPS 2023 and workshops at RSS, ICRA and NeurIPS 2021-2023
Organizer Computational Sensorimotor Learning (CSL) Seminar, MIT 2021
Outreach Officer GW6 2020, Lecturer 'Rishonot BaMada' 2017-2019, Coding instructor 'Project Mehamemet' 2016-2019, Mentor for undergrad freshmen 'Shalom Shana Alef' 2014-2015

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