281 citations · 1.2k across the 30 of their papers we have counts for
7 papers · 1 filter
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…
Bandits with Partially Observable Confounded Data
Guy Tennenholtz, Uri Shalit, Shie Mannor +1
We study linear contextual bandits with access to a large, confounded, offline dataset that was sampled from some fixed policy. We show that this problem is closely related to a va…
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Amir Feder, Nadav Oved, Uri Shalit +1
Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all machine learning based methods, they are as good as…
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.…
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Fredrik D. Johansson, Uri Shalit, Nathan Kallus +1
Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making. The cost and impracticality of performin…