6 citations · 9 across the 2 of their papers we have counts for
13 papers
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
Guy Tennenholtz, Assaf Hallak, Gal Dalal +3
We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expe…
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Junhyung Park, Uri Shalit, Bernhard Schölkopf +1
We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to en…
Using Deep Networks for Scientific Discovery in Physiological Signals
Tom Beer, Bar Eini-Porat, Sebastian Goodfellow +2
Deep neural networks (DNN) have shown remarkable success in the classification of physiological signals. In this study we propose a method for examining to what extent does a DNN's…
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
Andrew Jesson, Sören Mindermann, Uri Shalit +1
Recommending the best course of action for an individual is a major application of individual-level causal effect estimation. This application is often needed in safety-critical do…
A causal view of compositional zero-shot recognition
Yuval Atzmon, Felix Kreuk, Uri Shalit +1
People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domai…
Generative ODE Modeling with Known Unknowns
Ori Linial, Neta Ravid, Danny Eytan +1
In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes.…