no code implementations • 31 May 2023 • Ofir Nabati, Guy Tennenholtz, Shie Mannor
We present a representation-driven framework for reinforcement learning.
3 code implementations • 7 Feb 2021 • Ofir Nabati, Tom Zahavy, Shie Mannor
To alleviate this, we propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online.
no code implementations • 1 Jan 2021 • Tom Zahavy, Ofir Nabati, Leor Cohen, Shie Mannor
We study neural-linear bandits for solving problems where both exploration and representation learning play an important role.
no code implementations • 23 Feb 2020 • Tomer Galanti, Ofir Nabati, Lior Wolf
In the multivariate case, where one can ensure that the complexities of the cause and effect are balanced, we propose a new adversarial training method that mimics the disentangled structure of the causal model.
no code implementations • 25 Sep 2019 • Tomer Galanti, Ofir Nabati, Lior Wolf
Comparing the reconstruction errors of the two autoencoders, one for each variable, is shown to perform well on the accepted benchmarks of the field.
no code implementations • 31 Jan 2018 • Ofir Nabati, David Mendlovic, Raja Giryes
One of its drawbacks is the need for multi-lens in the imaging.