1 citations · 2 across the 3 of their papers we have counts for
6 papers
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…
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…
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov +1
Modeling uncertainty in deep neural networks, despite recent important advances, is still an open problem. Bayesian neural networks are a powerful solution, where the prior over ne…
Bayesian Structure Learning by Recursive Bootstrap
Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov +2
We address the problem of Bayesian structure learning for domains with hundreds of variables by employing non-parametric bootstrap, recursively. We propose a method that covers bot…
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz +2
We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth and inter-layer connectivity where conditi…