Aviv Tamar

Welcome to my homepage!

I’m an assistant professor at Technion – Israel Institute for Technology, where I hold the Robert J. Shillman Career Advancement Chair.

My research focuses on AI and machine learning, with an emphasis on robotics applications. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks, and how to learn behavior from interaction in an interpretable and safe manner. Most of my work falls under the framework of reinforcement learning, and its connections to representation learning, planning, and risk-averse optimization. See my publications page for more details!

Previously, I was a postdoc in the Berkeley AI Research Lab (BAIR) at UC Berkeley, with Prof. Pieter Abbeel. I completed my PhD. at the Technion, supervised by Prof. Shie Mannor, and my MSc also at Technion, under the supervision of Prof. Ron Meir.

News

11. Oct 2021

New preprint: Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability

11. Oct 2021

Offline meta learning of exploration got accepted to NeurIPS 2021!

1. Apr 2021

2 papers (contrastive domain randomization and efficient self-supervised data collection) got accepted to ICRA 2021, and Soft-Intro VAE accepted as an oral presentation at CVPR 2021!

3. Sep 2020

New preprint: offline meta reinforcement learning

3. Sep 2020

Our work on efficient MDP analysis for selfish-mining in blockchains got accepted to ACM Advances in Financial Technologies

21. June 2020

2 papers accepted to ICML 2020! Sub-goal Trees and Hallucinative Topological Memory

31. Mar 2020

Deep Residual Flow got accepted to CVPR 2020!

16. Jan 2020

New preprint: Deep Residual Flow for Novelty Detection

10. Dec 2019

I'll be giving a talk on visual planning and novelty detection at NeurIPS workshop: Safety and Robustness in Decision-making

... see all News