52 citations · 54 across the 5 of their papers we have counts for
9 papers
Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning
Dan Elbaz, Gal Novik, Oren Salzman
Offline reinforcement-learning (RL) algorithms learn to make decisions using a given, fixed training dataset without online data collection. This problem setting is captivating bec…
CLEAR: Causal Explanations from Attention in Neural Recommenders
Shami Nisimov, Raanan Y. Rohekar, Yaniv Gurwicz +2
We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders.…
Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper
Shami Nisimov, Yaniv Gurwicz, Raanan Y. Rohekar +1
Causal discovery from observational data is an important tool in many branches of science. Under certain assumptions it allows scientists to explain phenomena, predict, and make de…
Efficient Self-Supervised Data Collection for Offline Robot Learning
Shadi Endrawis, Gal Leibovich, Guy Jacob +2
A practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data, using some data collection policy, and then lear…
A Single Iterative Step for Anytime Causal Discovery
Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov +1
We present a sound and complete algorithm for recovering causal graphs from observed, non-interventional data, in the possible presence of latent confounders and selection bias. We…
Neural Network Distiller: A Python Package For DNN Compression Research
Neta Zmora, Guy Jacob, Lev Zlotnik +2
This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research. Distiller is a library of DNN co…