b'Uri Shalit'

DeepAI AI Chat

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research

∙ 04/20/2023

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Estimating heterogeneous treatment effects from observational data is a ...

0 Miruna Oprescu, et al. ∙

research

∙ 11/28/2022

Malign Overfitting: Interpolation Can Provably Preclude Invariance

Learned classifiers should often possess certain invariance properties m...

0 Yoav Wald, et al. ∙

research

∙ 05/30/2022

Reinforcement Learning with a Terminator

We present the problem of reinforcement learning with exogenous terminat...

0 Guy Tennenholtz, et al. ∙

research

∙ 04/21/2022

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

Estimating the effects of continuous-valued interventions from observati...

0 Andrew Jesson, et al. ∙

research

∙ 11/03/2021

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

Estimating personalized treatment effects from high-dimensional observat...

13 Andrew Jesson, et al. ∙

research

∙ 10/13/2021

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

We consider the problem of using expert data with unobserved confounders...

0 Guy Tennenholtz, et al. ∙

research

∙ 03/08/2021

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

We study the problem of learning conditional average treatment effects (...

0 Andrew Jesson, et al. ∙

research

∙ 02/20/2021

On Calibration and Out-of-domain Generalization

Out-of-domain (OOD) generalization is a significant challenge for machin...

0 Yoav Wald, et al. ∙

research

∙ 02/16/2021

Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

We propose to analyse the conditional distributional treatment effect (C...

8 Junhyung Park, et al. ∙

research

∙ 08/25/2020

Using Deep Networks for Scientific Discovery in Physiological Signals

Deep neural networks (DNN) have shown remarkable success in the classifi...

54 Tom Beer, et al. ∙

research

∙ 07/01/2020

Identifying Causal Effect Inference Failure with Uncertainty-Aware Models

Recommending the best course of action for an individual is a major appl...

5 Andrew Jesson, et al. ∙

research

∙ 06/25/2020

A causal view of compositional zero-shot recognition

People easily recognize new visual categories that are new combinations ...

18 Yuval Atzmon, et al. ∙

research

∙ 06/11/2020

Bandits with Partially Observable Offline Data

We study linear contextual bandits with access to a large, partially obs...

0 Guy Tennenholtz, et al. ∙

research

∙ 05/27/2020

CausaLM: Causal Model Explanation Through Counterfactual Language Models

Understanding predictions made by deep neural networks is notoriously di...

6 Amir Feder, et al. ∙

research

∙ 03/24/2020

Generative ODE Modeling with Known Unknowns

In several crucial applications, domain knowledge is encoded by a system...

5 Ori Linial, et al. ∙

research

∙ 01/21/2020

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects

Practitioners in diverse fields such as healthcare, economics and educat...

89 Fredrik D. Johansson, et al. ∙

research

∙ 10/01/2019

Robust learning with the Hilbert-Schmidt independence criterion

We investigate the use of a non-parametric independence measure, the Hil...

0 Daniel Greenfeld, et al. ∙

research

∙ 09/09/2019

Off-Policy Evaluation in Partially Observable Environments

This work studies the problem of batch off-policy evaluation for Reinfor...

0 Guy Tennenholtz, et al. ∙

research

∙ 07/16/2019

Explaining Classifiers with Causal Concept Effect (CaCE)

How can we understand classification decisions made by deep neural nets?...

0 Yash Goyal, et al. ∙

research

∙ 10/27/2018

Removing Hidden Confounding by Experimental Grounding

Observational data is increasingly used as a means for making individual...

0 Nathan Kallus, et al. ∙

research

∙ 10/19/2018

Harmonizing Fully Optimal Designs with Classic Randomization in Fixed Trial Experiments

There is a movement in design of experiments away from the classic rando...

0 Adam Kapelner, et al. ∙

research

∙ 02/23/2018

Learning Weighted Representations for Generalization Across Designs

Predictive models that generalize well under distributional shift are of...

0 Fredrik D. Johansson, et al. ∙

research

∙ 07/09/2017

Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

Statisticians have made great strides towards assumption-free estimation...

0 Vincent Dorie, et al. ∙

research

∙ 05/24/2017

Causal Effect Inference with Deep Latent-Variable Models

Learning individual-level causal effects from observational data, such a...

0 Christos Louizos, et al. ∙

research

∙ 09/30/2016

Structured Inference Networks for Nonlinear State Space Models

Gaussian state space models have been used for decades as generative mod...

0 Rahul G. Krishnan, et al. ∙

research

∙ 06/13/2016

Estimating individual treatment effect: generalization bounds and algorithms

There is intense interest in applying machine learning to problems of ca...

0 Uri Shalit, et al. ∙

research

∙ 05/12/2016

Learning Representations for Counterfactual Inference

Observational studies are rising in importance due to the widespread acc...

0 Fredrik D. Johansson, et al. ∙

research

∙ 11/16/2015

Deep Kalman Filters

Kalman Filters are one of the most influential models of time-varying ph...

1 Rahul G. Krishnan, et al. ∙

research

∙ 12/02/2013

Efficient coordinate-descent for orthogonal matrices through Givens rotations

Optimizing over the set of orthogonal matrices is a central component in...

0 Uri Shalit, et al. ∙