no code implementations • 27 Mar 2024 • Bar Eini Porat, Danny Eytan, Uri Shalit

This research paves the way for clinically relevant machine learning model evaluation and optimization, promising to improve ICU patient care.

2 code implementations • 20 Apr 2023 • Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit

There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data.

no code implementations • 28 Nov 2022 • Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

This suggests that the phenomenon of ``benign overfitting," in which models generalize well despite interpolating, might not favorably extend to settings in which robustness or fairness are desirable.

1 code implementation • 30 May 2022 • Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal

We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds.

2 code implementations • 21 Apr 2022 • Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics.

2 code implementations • NeurIPS 2021 • Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects.

no code implementations • ICLR 2022 • Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup.

1 code implementation • 8 Mar 2021 • Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders.

no code implementations • NeurIPS 2021 • Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit

In this work, we draw a link between OOD performance and model calibration, arguing that calibration across multiple domains can be viewed as a special case of an invariant representation leading to better OOD generalization.

no code implementations • 16 Feb 2021 • Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

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 encode a treatment's distributional aspects beyond the mean.

2 code implementations • 25 Aug 2020 • Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit

In this study we propose a method for examining to what extent does a DNN's performance rely on rediscovering existing features of the signals, as opposed to discovering genuinely new features.

1 code implementation • NeurIPS 2020 • Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

We show that our methods enable us to deal gracefully with situations of "no-overlap", common in high-dimensional data, where standard applications of causal effect approaches fail.

1 code implementation • NeurIPS 2020 • Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

This leads to consistent misclassification of samples from a new distribution, like new combinations of known components.

no code implementations • 11 Jun 2020 • Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

We construct a linear bandit algorithm that takes advantage of the projected information, and prove regret bounds.

1 code implementation • CL (ACL) 2021 • Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart

Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance.

3 code implementations • ICLR Workshop DeepDiffEq 2019 • Ori Linial, Neta Ravid, Danny Eytan, Uri Shalit

A motivating example is intensive care unit patients: the dynamics of vital physiological functions, such as the cardiovascular system with its associated variables (heart rate, cardiac contractility and output and vascular resistance) can be approximately described by a known system of ODEs.

no code implementations • 21 Jan 2020 • Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making.

1 code implementation • ICML 2020 • Daniel Greenfeld, Uri Shalit

We adapt it to the task of learning for unsupervised covariate shift: learning on a source domain without access to any instances or labels from the unknown target domain, but with the assumption that $p(y|x)$ (the conditional probability of labels given instances) remains the same in the target domain.

no code implementations • 9 Sep 2019 • Guy Tennenholtz, Shie Mannor, Uri Shalit

This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments.

no code implementations • 16 Jul 2019 • Yash Goyal, Amir Feder, Uri Shalit, Been Kim

To overcome this problem, we define the Causal Concept Effect (CaCE) as the causal effect of (the presence or absence of) a human-interpretable concept on a deep neural net's predictions.

no code implementations • NeurIPS 2018 • Nathan Kallus, Aahlad Manas Puli, Uri Shalit

We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data.

no code implementations • ICLR 2018 • Fredrik D. Johansson, Nathan Kallus, Uri Shalit, David Sontag

We pose both of these problems as prediction under a shift in design.

no code implementations • 9 Jul 2017 • Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone

Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions.

6 code implementations • NeurIPS 2017 • Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers.

Ranked #9 on Causal Inference on IHDP

3 code implementations • 30 Sep 2016 • Rahul G. Krishnan, Uri Shalit, David Sontag

We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.

Ranked #5 on Multivariate Time Series Forecasting on USHCN-Daily

4 code implementations • ICML 2017 • Uri Shalit, Fredrik D. Johansson, David Sontag

We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation.

Ranked #3 on Causal Inference on Jobs

1 code implementation • 12 May 2016 • Fredrik D. Johansson, Uri Shalit, David Sontag

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology.

3 code implementations • 16 Nov 2015 • Rahul G. Krishnan, Uri Shalit, David Sontag

Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.

no code implementations • 2 Dec 2013 • Uri Shalit, Gal Chechik

Optimizing over the set of orthogonal matrices is a central component in problems like sparse-PCA or tensor decomposition.

no code implementations • NeurIPS 2010 • Uri Shalit, Daphna Weinshall, Gal Chechik

When learning models that are represented in matrix forms, enforcing a low-rank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model.

no code implementations • NeurIPS 2009 • Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio

We describe OASIS, a method for learning pairwise similarity that is fast and scales linearly with the number of objects and the number of non-zero features.

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