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Uri Shalit
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2020 – today
- 2024
- [i29]Bar Eini-Porat, Danny Eytan, Uri Shalit:
Aiming for Relevance. CoRR abs/2403.18668 (2024) - [i28]Ori Linial, Guy Tennenholtz, Uri Shalit:
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. CoRR abs/2407.00806 (2024) - [i27]Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova, Uri Shalit, Christopher C. Holmes:
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition. CoRR abs/2409.07215 (2024) - 2023
- [j8]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) - [c29]Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon:
Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. ICLR 2023 - [c28]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: 26599-26618 - [i26]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 abs/2304.10577 (2023) - 2022
- [j7]Bar Eini-Porat, Ofra Amir, Danny Eytan
Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. J. Biomed. Informatics 132: 104107 (2022) - [j6]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: 166:1-166:50 (2022) - [c27]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 - [c26]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 - [c25]Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal:
Reinforcement Learning with a Terminator. NeurIPS 2022 - [i25]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 abs/2204.10022 (2022) - [i24]Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal:
Reinforcement Learning with a Terminator. CoRR abs/2205.15376 (2022) - [i23]Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon:
Malign Overfitting: Interpolation Can Provably Preclude Invariance. CoRR abs/2211.15724 (2022) - 2021
- [j5]Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart:
CausaLM: Causal Model Explanation Through Counterfactual Language Models. Comput. Linguistics 47(2): 333-386 (2021) - [j4]Michael Roimi, Rom Gutman, Jonathan Somer, Asaf Ben Arie, Ido Calman, Yaron Bar-Lavie, Udi Gelbshtein, Sigal Liverant-Taub, Arnona Ziv, Danny Eytan
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): 1188-1196 (2021) - [c24]Ori Linial
Generative ODE modeling with known unknowns. CHIL 2021: 79-94 - [c23]Tom Ron, Omer Ben-Porat, Uri Shalit:
Corporate Social Responsibility via Multi-Armed Bandits. FAccT 2021: 26-40 - [c22]Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit:
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML 2021: 4829-4838 - [c21]Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet:
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML 2021: 8401-8412 - [c20]Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit:
On Calibration and Out-of-Domain Generalization. NeurIPS 2021: 2215-2227 - [c19]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: 30465-30478 - [c18]Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni:
Bandits with partially observable confounded data. UAI 2021: 430-439 - [i22]Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet:
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. CoRR abs/2102.08208 (2021) - [i21]Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit:
On Calibration and Out-of-domain Generalization. CoRR abs/2102.10395 (2021) - [i20]Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit:
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR abs/2103.04850 (2021) - [i19]Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit:
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. CoRR abs/2110.06539 (2021) - [i18]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 abs/2111.02275 (2021) - 2020
- [c17]Guy Tennenholtz, Uri Shalit, Shie Mannor:
Off-Policy Evaluation in Partially Observable Environments. AAAI 2020: 10276-10283 - [c16]Daniel Greenfeld, Uri Shalit:
Robust Learning with the Hilbert-Schmidt Independence Criterion. ICML 2020: 3759-3768 - [c15]Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit:
Using deep networks for scientific discovery in physiological signals. MLHC 2020: 685-709 - [c14]Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik:
A causal view of compositional zero-shot recognition. NeurIPS 2020 - [c13]Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS 2020 - [i17]Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag:
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR abs/2001.07426 (2020) - [i16]Ori Linial, Danny Eytan, Uri Shalit:
Generative ODE Modeling with Known Unknowns. CoRR abs/2003.10775 (2020) - [i15]Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart:
CausaLM: Causal Model Explanation Through Counterfactual Language Models. CoRR abs/2005.13407 (2020) - [i14]Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni:
Bandits with Partially Observable Offline Data. CoRR abs/2006.06731 (2020) - [i13]Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik:
A causal view of compositional zero-shot recognition. CoRR abs/2006.14610 (2020) - [i12]Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR abs/2007.00163 (2020) - [i11]Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit:
Using Deep Networks for Scientific Discovery in Physiological Signals. CoRR abs/2008.10936 (2020)
2010 – 2019
- 2019
- [c12]Galia Nordon, Gideon Koren
Building Causal Graphs from Medical Literature and Electronic Medical Records. AAAI 2019: 1102-1109 - [i10]Yash Goyal, Uri Shalit, Been Kim:
Explaining Classifiers with Causal Concept Effect (CaCE). CoRR abs/1907.07165 (2019) - [i9]Guy Tennenholtz, Shie Mannor, Uri Shalit:
Off-Policy Evaluation in Partially Observable Environments. CoRR abs/1909.03739 (2019) - [i8]Daniel Greenfeld, Uri Shalit:
Robust learning with the Hilbert-Schmidt independence criterion. CoRR abs/1910.00270 (2019) - 2018
- [c11]Nathan Kallus, Aahlad Manas Puli, Uri Shalit:
Removing Hidden Confounding by Experimental Grounding. NeurIPS 2018: 10911-10920 - [i7]Nathan Kallus, Aahlad Manas Puli, Uri Shalit:
Removing Hidden Confounding by Experimental Grounding. CoRR abs/1810.11646 (2018) - 2017
- [c10]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Structured Inference Networks for Nonlinear State Space Models. AAAI 2017: 2101-2109 - [c9]Uri Shalit, Fredrik D. Johansson, David A. Sontag:
Estimating individual treatment effect: generalization bounds and algorithms. ICML 2017: 3076-3085 - [c8]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: 6446-6456 - [i6]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. CoRR abs/1705.08821 (2017) - 2016
- [c7]Fredrik D. Johansson, Uri Shalit, David A. Sontag:
Learning Representations for Counterfactual Inference. ICML 2016: 3020-3029 - [i5]Fredrik D. Johansson, Uri Shalit, David A. Sontag:
Learning Representations for Counterfactual Inference. CoRR abs/1605.03661 (2016) - [i4]Uri Shalit, Fredrik D. Johansson, David A. Sontag:
Bounding and Minimizing Counterfactual Error. CoRR abs/1606.03976 (2016) - [i3]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Structured Inference Networks for Nonlinear State Space Models. CoRR abs/1609.09869 (2016) - 2015
- [b1]Uri Shalit:
Scalable streaming learning of dyadic relationships (שער נוסף בעברית: למידת יחסים דיאדיים ממידע זורם.). Hebrew University of Jerusalem, Israel, 2015 - [c6]Yuval Atzmon, Uri Shalit, Gal Chechik:
Learning Sparse Metrics, One Feature at a Time. FE@NIPS 2015: 30-48 - [i2]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Deep Kalman Filters. CoRR abs/1511.05121 (2015) - 2014
- [c5]Uri Shalit, Gal Chechik:
Coordinate-descent for learning orthogonal matrices through Givens rotations. ICML 2014: 548-556 - 2013
- [j3]Noa Liscovitch, Uri Shalit, Gal Chechik
FuncISH: learning a functional representation of neural ISH images. Bioinform. 29(13): 36-43 (2013) - [c4]Uri Shalit, Daphna Weinshall, Gal Chechik:
Modeling Musical Influence with Topic Models. ICML (2) 2013: 244-252 - [i1]Uri Shalit, Gal Chechik:
Efficient coordinate-descent for orthogonal matrices through Givens rotations. CoRR abs/1312.0624 (2013) - 2012
- [j2]Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in the Embedded Manifold of Low-rank Matrices. J. Mach. Learn. Res. 13: 429-458 (2012) - 2010
- [j1]Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio:
Large Scale Online Learning of Image Similarity Through Ranking. J. Mach. Learn. Res. 11: 1109-1135 (2010) - [c3]Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in The Manifold of Low-Rank Matrices. NIPS 2010: 2128-2136
2000 – 2009
- 2009
- [c2]Gal Chechik
Large Scale Online Learning of Image Similarity through Ranking. IbPRIA 2009: 11-14 - [c1]Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio:
An Online Algorithm for Large Scale Image Similarity Learning. NIPS 2009: 306-314
Coauthor Index
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