Publications

Tell me something interesting: Clinical utility of machine learning prediction models in the ICU

Bar Eini-Porat, Ofra Amir, Danny Eytan, Uri Shalit

Journal of Biomedical Informatics, 2022


On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

ICLR 2022


Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational DataOn Calibration and Out-of-domain Generalization

Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

NeurIPS 2021


On Calibration and Out-of-domain Generalization

Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit

NeurIPS 2021


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

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

ICML 2021


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

Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

ICML 2021


COVID-19 dynamics after a national immunization program in Israel

Hagai Rossman, Smadar Shilo, Tomer Meir, Malka Gorfine, Uri Shalit, Eran Segal

Nature Medicine, 2021


Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study

Gil Caspi, Avshalom Dayan, Yael Eshal, Sigal Liverant-Taub, Gilad Twig, Uri Shalit, Yair Lewis, Avi Shina, Oren Caspi

Clinical Microbiology and Infection, 2021


Hospital load and increased COVID-19 related mortality in Israel

Hagai Rossman, Tomer Meir, Jonathan Somer, Smadar Shilo, Rom Gutman, Asaf Ben Arie, Eran Segal, Uri Shalit, Malka Gorfine

Nature Communications volume 12, Article number: 1904, 2021


Development and validation of a machine learning model for predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients – a nationwide study

Michael Roimi, Rom Gutman, Jonathan Somer, Asaf Ben Arie, Ido Calman, Yaron Bar-Lavie, Udi Gelbshtein, Sigal Liverant-Taub, Arnona Ziv, Danny Eytan, Malka Gorfine, Uri Shalit

Journal of the American Medical Informatics Association, Volume 28, Issue 6, June 2021, Pages 1188–1196


Using deep networks for scientific discovery in physiological signals

Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit

ML4H: Proceedings of Machine Learning Research 126:1–24, 2020


Identifying Causal Effect Inference Failure with Uncertainty-Aware Models

Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

NeurIPS 2020


A causal view of compositional zero-shot recognition

Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

NeurIPS 2020


Bandits with Partially Observable Offline Data

Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

UAI 2021


CausaLM: Causal Model Explanation Through Counterfactual Language Models

Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart

Journal of Computational Linguistics, 2021


Can we learn individual-level treatment policies from clinical data?

Uri Shalit

Biostatistics 21 (2), 359-362, 2020


Generative ODE Modeling with Known Unknowns

Ori Linial, Neta Ravid, Danny Eytan, Uri Shalit

Proceedings of the Conference on Health, Inference, and Learning (CHIL), 2021


Developing a COVID-19 mortality risk prediction model when individual level data is not available

Noam Barda, Dan Riesel, Amichay Akriv, Joseph Levi, Uriah Finkel, Gal Yona, Daniel Greenfeld, Shimon Sheiba, Jonathan Somer, Eitan Bachmat, Guy N Rothblum, Uri Shalit, Doron Netzer, Ran Balicer, Noa Dagan

Nature Communications 114439 (2020).


Robust learning with the Hilbert-Schmidt independence criterion

Daniel Greenfeld, Uri Shalit.

In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020
Code


Off-Policy Evaluation in Partially Observable Environments

Guy Tennenholtz, Shie Mannor, Uri Shalit

In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020


Building Causal Graphs from Medical Literature and Electronic Medical Records

Galia Nordon, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, Kira Radinsky

In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019


Harmonizing Fully Optimal Designs with Classic Randomization in Fixed Trial Experiments

Adam Kapelner, Abba M Krieger, Uri Shalit, David Azriel

The American Statistician, 1-12, 2020


Learning Weighted Representations for Generalization Across Designs

Fredrik D Johansson, Nathan Kallus, Uri Shalit, David Sontag

2018


Removing Hidden Confounding by Experimental Grounding

Nathan Kallus, Aahlad Manas Puli, Uri Shalit

In Proceedings of the 32nd International Conference on Neural Information Processing Systems, of NeurIPS’18, 2018


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

Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone

Statistical Science 34 (1), 43-68, 2018


Causal effect inference with deep latent-variable models

Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Rich Zemel, Max Welling

In Proceedings of the 31st International Conference on Neural Information Processing Systems, of NeurIPS’17, 2017


Structured Inference Networks for Nonlinear State Space Models

Rahul G Krishnan, Uri Shalit, David Sontag

In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017


Estimating individual treatment effect: generalization bounds and algorithms

Uri Shalit, Fredrik D Johansson, David Sontag

In Proceedings of the 34th International Conference on Machine Learning (ICML), 2017


Learning representations for counterfactual inference

Fredrik D Johansson, Uri Shalit, David Sontag

In Proceedings of The 33rd International Conference on Machine Learning (ICML), 2016


Deep Kalman Filters

Rahul G Krishnan, Uri Shalit, David Sontag

2015


Learning Sparse Metrics, One Feature at a Time

Yuval Atzmon, Uri Shalit, Gal Chechik

Journal of Machine Learning Research, 2015


Coordinate-descent for learning orthogonal matrices through Givens rotations

Uri Shalit, Gal Chechik

In International Conference on Machine Learning (ICML), 2014


FuncISH: learning a functional representation of neural ISH images

Noa Liscovitch, Uri Shalit, Gal Chechik

Bioinformatics, 2013


Modeling Musical Influence with Topic Models

Uri Shalit, Daphna Weinshall, Gal Chechik

In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013


Descending Systems Translate Transient Cortical Commands into a Sustained Muscle Activation Signal

Uri Shalit, Nofya Zinger, Mati Joshua, Yifat Prut

Cerebral Cortex, 2012


Online Learning in the Embedded Manifold of Low-rank Matrices

Uri Shalit, Daphna Weinshall, Gal Chechik

Journal of Machine Learning Research (JMLR), 2012


Large scale online learning of image similarity through ranking

Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio

Journal of Machine Learning Research (JMLR), 2010


Online learning in the manifold of low-rank matrices

Uri Shalit, Daphna Weinshall, Gal Chechik

In Advances in Neural Information Processing Systems, 2010


An online algorithm for large scale image similarity learning

Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio

In Advances in Neural Information Processing Systems, 2009


Computation in spinal circuitry: Lessons from behaving primates

Ran Harel, Itay Asher, Oren Cohen, Zvi Israel, Uri Shalit, Yuval Yanai, Nofya Zinger, Yifat Prut

Behavioural Brain Research, 2008


Using topic modeling to detect and quantify semantic change

Haim Dubossarsky, Uri Shalit, Eitan Grossman, Daphna Weinshall

New Developments in the Quantitative Study of Languages