Uri Shalit

According to our database, Uri Shalit authored at least 49 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

On the ERM Principle in Meta-Learning.

Yannay Alon, Steve Hanneke, Shay Moran, Uri Shalit

CoRR, 2024

Is merging worth it? Securely evaluating the information gain for causal dataset acquisition.

Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova, Uri Shalit, Christopher C. Holmes

CoRR, 2024

Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators.

Ori Linial, Guy Tennenholtz, Uri Shalit

CoRR, 2024

Aiming for Relevance.

Bar Eini-Porat, Danny Eytan, Uri Shalit

CoRR, 2024

2023

iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside.

Neta Ravid Tannenbaum, Omer Gottesman, Azadeh Assadi, Mjaye Mazwi, Uri Shalit, Danny Eytan

PLoS Comput. Biol., 2023

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

Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit

Proceedings of the International Conference on Machine Learning, 2023

Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds.

Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

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

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

J. Mach. Learn. Res., 2022

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

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

J. Biomed. Informatics, 2022

Malign Overfitting: Interpolation Can Provably Preclude Invariance.

Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

CoRR, 2022

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

Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

CoRR, 2022

Reinforcement Learning with a Terminator.

Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 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

J. Am. Medical Informatics Assoc., 2021

CausaLM: Causal Model Explanation Through Counterfactual Language Models.

Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart

Comput. Linguistics, 2021

Bandits with partially observable confounded data.

Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

On Calibration and Out-of-Domain Generalization.

Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

Proceedings of the 38th International Conference on Machine Learning, 2021

Corporate Social Responsibility via Multi-Armed Bandits.

Tom Ron, Omer Ben-Porat, Uri Shalit

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Generative ODE modeling with known unknowns.

Ori Linial, Neta Ravid, Danny Eytan, Uri Shalit

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020

Bandits with Partially Observable Offline Data.

Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

CoRR, 2020

Generative ODE Modeling with Known Unknowns.

Ori Linial, Danny Eytan, Uri Shalit

CoRR, 2020

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models.

Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A causal view of compositional zero-shot recognition.

Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Using deep networks for scientific discovery in physiological signals.

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

Proceedings of the Machine Learning for Healthcare Conference, 2020

Robust Learning with the Hilbert-Schmidt Independence Criterion.

Daniel Greenfeld, Uri Shalit

Proceedings of the 37th International Conference on Machine Learning, 2020

Off-Policy Evaluation in Partially Observable Environments.

Guy Tennenholtz, Uri Shalit, Shie Mannor

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

2019

Explaining Classifiers with Causal Concept Effect (CaCE).

Yash Goyal, Uri Shalit, Been Kim

CoRR, 2019

Building Causal Graphs from Medical Literature and Electronic Medical Records.

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

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

2018

Removing Hidden Confounding by Experimental Grounding.

Nathan Kallus, Aahlad Manas Puli, Uri Shalit

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Causal Effect Inference with Deep Latent-Variable Models.

Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Estimating individual treatment effect: generalization bounds and algorithms.

Uri Shalit, Fredrik D. Johansson, David A. Sontag

Proceedings of the 34th International Conference on Machine Learning, 2017

Structured Inference Networks for Nonlinear State Space Models.

Rahul G. Krishnan, Uri Shalit, David A. Sontag

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

2016

Bounding and Minimizing Counterfactual Error.

Uri Shalit, Fredrik D. Johansson, David A. Sontag

CoRR, 2016

Learning Representations for Counterfactual Inference.

Fredrik D. Johansson, Uri Shalit, David A. Sontag

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Scalable streaming learning of dyadic relationships (שער נוסף בעברית: למידת יחסים דיאדיים ממידע זורם.).

Uri Shalit

PhD thesis, 2015

Deep Kalman Filters.

Rahul G. Krishnan, Uri Shalit, David A. Sontag

CoRR, 2015

Learning Sparse Metrics, One Feature at a Time.

Yuval Atzmon, Uri Shalit, Gal Chechik

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

2014

Coordinate-descent for learning orthogonal matrices through Givens rotations.

Uri Shalit, Gal Chechik

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Efficient coordinate-descent for orthogonal matrices through Givens rotations.

Uri Shalit, Gal Chechik

CoRR, 2013

FuncISH: learning a functional representation of neural ISH images.

Noa Liscovitch, Uri Shalit, Gal Chechik

Bioinform., 2013

Modeling Musical Influence with Topic Models.

Uri Shalit, Daphna Weinshall, Gal Chechik

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Online Learning in the Embedded Manifold of Low-rank Matrices.

Uri Shalit, Daphna Weinshall, Gal Chechik

J. Mach. Learn. Res., 2012

2010

Large Scale Online Learning of Image Similarity Through Ranking.

Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio

J. Mach. Learn. Res., 2010

Online Learning in The Manifold of Low-Rank Matrices.

Uri Shalit, Daphna Weinshall, Gal Chechik

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

An Online Algorithm for Large Scale Image Similarity Learning.

Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009