Uri Shalit

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 54

Top Topics

Reinforcement Learning

Observational Data

Top Venues

Publications

Bar Eini-Porat, Danny Eytan, Uri Shalit
Aiming for Relevance. CoRR (2024)
Ori Linial, Guy Tennenholtz, Uri Shalit
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. CoRR (2024)
Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. CoRR (2023)
Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. ICLR (2023)
Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. ICML (2023)
Neta Ravid Tannenbaum, Omer Gottesman, Azadeh Assadi, Mjaye Mazwi, Uri Shalit, Danny Eytan
iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. PLoS Comput. Biol. 19 (9) (2023)
Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
Malign Overfitting: Interpolation Can Provably Preclude Invariance. CoRR (2022)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
Reinforcement Learning with a Terminator. NeurIPS (2022)
Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. ICLR (2022)
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. J. Mach. Learn. Res. 23 (2022)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
Reinforcement Learning with a Terminator. CoRR (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR (2022)
Bar Eini-Porat, Ofra Amir, Danny Eytan, Uri Shalit
Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. J. Biomed. Informatics 132 (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS (2022)
Tom Ron, Omer Ben-Porat, Uri Shalit
Corporate Social Responsibility via Multi-Armed Bandits. FAccT (2021)
Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit
On Calibration and Out-of-domain Generalization. CoRR (2021)
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS (2021)
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
Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study. J. Am. Medical Informatics Assoc. 28 (6) (2021)
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR (2021)
Ori Linial, Neta Ravid, Danny Eytan, Uri Shalit
Generative ODE modeling with known unknowns. CHIL (2021)
Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. CoRR (2021)
Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. CoRR (2021)
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR (2021)
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML (2021)
Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit
On Calibration and Out-of-Domain Generalization. NeurIPS (2021)
Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart
CausaLM: Causal Model Explanation Through Counterfactual Language Models. Comput. Linguistics 47 (2) (2021)
Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML (2021)
Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni
Bandits with partially observable confounded data. UAI (2021)
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR (2020)
Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS (2020)
Guy Tennenholtz, Uri Shalit, Shie Mannor
Off-Policy Evaluation in Partially Observable Environments. AAAI (2020)
Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR (2020)
Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik
A causal view of compositional zero-shot recognition. CoRR (2020)
Daniel Greenfeld, Uri Shalit
Robust Learning with the Hilbert-Schmidt Independence Criterion. ICML (2020)
Ori Linial, Danny Eytan, Uri Shalit
Generative ODE Modeling with Known Unknowns. CoRR (2020)
Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni
Bandits with Partially Observable Offline Data. CoRR (2020)
Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit
Using Deep Networks for Scientific Discovery in Physiological Signals. CoRR (2020)
Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik
A causal view of compositional zero-shot recognition. NeurIPS (2020)
Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit
Using deep networks for scientific discovery in physiological signals. MLHC (2020)
Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart
CausaLM: Causal Model Explanation Through Counterfactual Language Models. CoRR (2020)
Daniel Greenfeld, Uri Shalit
Robust learning with the Hilbert-Schmidt independence criterion. CoRR (2019)
Galia Nordon, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, Kira Radinsky
Building Causal Graphs from Medical Literature and Electronic Medical Records. AAAI (2019)
Yash Goyal, Uri Shalit, Been Kim
Explaining Classifiers with Causal Concept Effect (CaCE). CoRR (2019)
Guy Tennenholtz, Shie Mannor, Uri Shalit
Off-Policy Evaluation in Partially Observable Environments. CoRR (2019)
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Removing Hidden Confounding by Experimental Grounding. NeurIPS (2018)
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Removing Hidden Confounding by Experimental Grounding. CoRR (2018)
Rahul G. Krishnan, Uri Shalit, David A. Sontag
Structured Inference Networks for Nonlinear State Space Models. AAAI (2017)
Uri Shalit, Fredrik D. Johansson, David A. Sontag
Estimating individual treatment effect: generalization bounds and algorithms. ICML (2017)
Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
Causal Effect Inference with Deep Latent-Variable Models. NIPS (2017)
Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
Causal Effect Inference with Deep Latent-Variable Models. CoRR (2017)
Rahul G. Krishnan, Uri Shalit, David A. Sontag
Structured Inference Networks for Nonlinear State Space Models. CoRR (2016)
Uri Shalit, Fredrik D. Johansson, David A. Sontag
Bounding and Minimizing Counterfactual Error. CoRR (2016)
Fredrik D. Johansson, Uri Shalit, David A. Sontag
Learning Representations for Counterfactual Inference. CoRR (2016)
Fredrik D. Johansson, Uri Shalit, David A. Sontag
Learning Representations for Counterfactual Inference. ICML (2016)
Yuval Atzmon, Uri Shalit, Gal Chechik
Learning Sparse Metrics, One Feature at a Time. FE@NIPS (2015)
Rahul G. Krishnan, Uri Shalit, David A. Sontag
Deep Kalman Filters. CoRR (2015)
Uri Shalit, Gal Chechik
Coordinate-descent for learning orthogonal matrices through Givens rotations. ICML (2014)
Uri Shalit, Gal Chechik
Efficient coordinate-descent for orthogonal matrices through Givens rotations. CoRR (2013)
Uri Shalit, Daphna Weinshall, Gal Chechik
Modeling Musical Influence with Topic Models. ICML (2) (2013)
Noa Liscovitch, Uri Shalit, Gal Chechik
FuncISH: learning a functional representation of neural ISH images. Bioinform. 29 (13) (2013)
Uri Shalit, Daphna Weinshall, Gal Chechik
Online Learning in the Embedded Manifold of Low-rank Matrices. J. Mach. Learn. Res. 13 (2012)
Uri Shalit, Daphna Weinshall, Gal Chechik
Online Learning in The Manifold of Low-Rank Matrices. NIPS (2010)
Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio
Large Scale Online Learning of Image Similarity Through Ranking. J. Mach. Learn. Res. 11 (2010)
Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio
An Online Algorithm for Large Scale Image Similarity Learning. NIPS (2009)
Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio
Large Scale Online Learning of Image Similarity through Ranking. IbPRIA (2009)