Uri Shalit - RankMe

81 papers A* 22A 3Misc 1Journal 45Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Preference-based Conditional Treatment Effects and Policy Learning. CoRR Dovid Parnas, Mathieu Even, Julie Josse, Uri Shalit
2026	J	`jnl`	Structured Hybrid Mechanistic Models for Robust Estimation of Time-Dependent Intervention Outcomes. CoRR Tomer Meir, Ori Linial, Danny Eytan, Uri Shalit
2025	—	`conf`	BIG-Bench Extra Hard. ACL (1) Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K. Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V. Le, Orhan Firat
2025	J	`jnl`	BIG-Bench Extra Hard. CoRR Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Kshitij Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V. Le, Orhan Firat
2025	J	`jnl`	From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies. CoRR Rom Gutman, Shimon Sheiba, Omer Noy Klein, Naama Dekel Bird, Amit Gruber, Doron Aronson, Oren Caspi, Uri Shalit
2025	A*	`conf`	Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees. ICML Tomer Meir, Uri Shalit, Malka Gorfine
2025	J	`jnl`	Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees. CoRR Tomer Meir, Uri Shalit, Malka Gorfine
2025	A	`conf`	Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition. AISTATS Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes
2025	A*	`conf`	Set Valued Predictions For Robust Domain Generalization. ICML Ron Tsibulsky, Daniel Nevo, Uri Shalit
2025	J	`jnl`	Set Valued Predictions For Robust Domain Generalization. CoRR Ron Tsibulsky, Daniel Nevo, Uri Shalit
2025	A	`conf`	Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions. AISTATS Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit, Mihaela van der Schaar
2025	J	`jnl`	Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions. CoRR Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit, Mihaela van der Schaar
2024	J	`jnl`	Aiming for Relevance. CoRR Bar Eini-Porat, Danny Eytan, Uri Shalit
2024	J	`jnl`	Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. CoRR Ori Linial, Guy Tennenholtz, Uri Shalit
2024	J	`jnl`	Is merging worth it? Securely evaluating the information gain for causal dataset acquisition. CoRR Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes
2024	J	`jnl`	On the ERM Principle in Meta-Learning. CoRR Yannay Alon, Steve Hanneke, Shay Moran, Uri Shalit
2024	A*	`conf`	When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding. NeurIPS Marah Ghoummaid, Uri Shalit
2023	A*	`conf`	B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. ICML Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
2023	J	`jnl`	B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. CoRR Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
2023	A*	`conf`	Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. ICLR Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
2023	J	`jnl`	iCVS - Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. PLoS Comput. Biol. Neta Ravid Tannenbaum, Omer Gottesman, Azadeh Assadi, Mjaye Mazwi, Uri Shalit, Danny Eytan
2022	J	`jnl`	Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. J. Mach. Learn. Res. Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
2022	J	`jnl`	Malign Overfitting: Interpolation Can Provably Preclude Invariance. CoRR Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
2022	A*	`conf`	On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. ICLR Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit
2022	A*	`conf`	Reinforcement Learning with a Terminator. NeurIPS Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
2022	J	`jnl`	Reinforcement Learning with a Terminator. CoRR Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
2022	A*	`conf`	Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2022	J	`jnl`	Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2022	J	`jnl`	Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. J. Biomed. Informatics Bar Eini-Porat, Ofra Amir, Danny Eytan, Uri Shalit
2021	A	`conf`	Bandits with partially observable confounded data. UAI Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni
2021	J	`jnl`	CausaLM: Causal Model Explanation Through Counterfactual Language Models. Comput. Linguistics Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart
2021	A*	`conf`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	A*	`conf`	Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
2021	J	`jnl`	Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. CoRR Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
2021	—	`conf`	Corporate Social Responsibility via Multi-Armed Bandits. FAccT Tom Ron, Omer Ben-Porat, Uri Shalit
2021	J	`jnl`	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. 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
2021	—	`conf`	Generative ODE modeling with known unknowns. CHIL Ori Linial, Neta Ravid, Danny Eytan, Uri Shalit
2021	A*	`conf`	On Calibration and Out-of-Domain Generalization. NeurIPS Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit
2021	J	`jnl`	On Calibration and Out-of-domain Generalization. CoRR Yoav Wald, Amir Feder, Daniel Greenfeld, Uri Shalit
2021	J	`jnl`	On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. CoRR Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit
2021	A*	`conf`	Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
2021	J	`jnl`	Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
2020	A*	`conf`	A causal view of compositional zero-shot recognition. NeurIPS Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik
2020	J	`jnl`	A causal view of compositional zero-shot recognition. CoRR Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik
2020	J	`jnl`	Bandits with Partially Observable Offline Data. CoRR Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni
2020	J	`jnl`	CausaLM: Causal Model Explanation Through Counterfactual Language Models. CoRR Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart
2020	J	`jnl`	Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
2020	J	`jnl`	Generative ODE Modeling with Known Unknowns. CoRR Ori Linial, Danny Eytan, Uri Shalit
2020	J	`jnl`	Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
2020	A*	`conf`	Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
2020	A*	`conf`	Off-Policy Evaluation in Partially Observable Environments. AAAI Guy Tennenholtz, Uri Shalit, Shie Mannor
2020	A*	`conf`	Robust Learning with the Hilbert-Schmidt Independence Criterion. ICML Daniel Greenfeld, Uri Shalit
2020	J	`jnl`	Using Deep Networks for Scientific Discovery in Physiological Signals. CoRR Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit
2020	—	`conf`	Using deep networks for scientific discovery in physiological signals. MLHC Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit
2019	A*	`conf`	Building Causal Graphs from Medical Literature and Electronic Medical Records. AAAI Galia Nordon, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, Kira Radinsky
2019	J	`jnl`	Explaining Classifiers with Causal Concept Effect (CaCE). CoRR Yash Goyal, Uri Shalit, Been Kim
2019	J	`jnl`	Off-Policy Evaluation in Partially Observable Environments. CoRR Guy Tennenholtz, Shie Mannor, Uri Shalit
2019	J	`jnl`	Robust learning with the Hilbert-Schmidt independence criterion. CoRR Daniel Greenfeld, Uri Shalit
2018	A*	`conf`	Removing Hidden Confounding by Experimental Grounding. NeurIPS Nathan Kallus, Aahlad Manas Puli, Uri Shalit
2018	J	`jnl`	Removing Hidden Confounding by Experimental Grounding. CoRR Nathan Kallus, Aahlad Manas Puli, Uri Shalit
2017	—	`conf`	Causal Effect Inference with Deep Latent-Variable Models. NIPS Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	J	`jnl`	Causal Effect Inference with Deep Latent-Variable Models. CoRR Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	A*	`conf`	Estimating individual treatment effect: generalization bounds and algorithms. ICML Uri Shalit, Fredrik D. Johansson, David A. Sontag
2017	A*	`conf`	Structured Inference Networks for Nonlinear State Space Models. AAAI Rahul G. Krishnan, Uri Shalit, David A. Sontag
2016	J	`jnl`	Bounding and Minimizing Counterfactual Error. CoRR Uri Shalit, Fredrik D. Johansson, David A. Sontag
2016	A*	`conf`	Learning Representations for Counterfactual Inference. ICML Fredrik D. Johansson, Uri Shalit, David A. Sontag
2016	J	`jnl`	Learning Representations for Counterfactual Inference. CoRR Fredrik D. Johansson, Uri Shalit, David A. Sontag
2016	J	`jnl`	Structured Inference Networks for Nonlinear State Space Models. CoRR Rahul G. Krishnan, Uri Shalit, David A. Sontag
2015	J	`jnl`	Deep Kalman Filters. CoRR Rahul G. Krishnan, Uri Shalit, David A. Sontag
2015	—	`conf`	Learning Sparse Metrics, One Feature at a Time. FE@NIPS Yuval Atzmon, Uri Shalit, Gal Chechik
2015	—	`—`	Scalable streaming learning of dyadic relationships (שער נוסף בעברית: למידת יחסים דיאדיים ממידע זורם.). Uri Shalit
2014	A*	`conf`	Coordinate-descent for learning orthogonal matrices through Givens rotations. ICML Uri Shalit, Gal Chechik
2013	J	`jnl`	Efficient coordinate-descent for orthogonal matrices through Givens rotations. CoRR Uri Shalit, Gal Chechik
2013	J	`jnl`	FuncISH: learning a functional representation of neural ISH images. Bioinform. Noa Liscovitch, Uri Shalit, Gal Chechik
2013	—	`conf`	Modeling Musical Influence with Topic Models. ICML (2) Uri Shalit, Daphna Weinshall, Gal Chechik
2012	J	`jnl`	Online Learning in the Embedded Manifold of Low-rank Matrices. J. Mach. Learn. Res. Uri Shalit, Daphna Weinshall, Gal Chechik
2010	J	`jnl`	Large Scale Online Learning of Image Similarity Through Ranking. J. Mach. Learn. Res. Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio
2010	—	`conf`	Online Learning in The Manifold of Low-Rank Matrices. NIPS Uri Shalit, Daphna Weinshall, Gal Chechik
2009	—	`conf`	An Online Algorithm for Large Scale Image Similarity Learning. NIPS Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio
2009	Misc	`conf`	Large Scale Online Learning of Image Similarity through Ranking. IbPRIA Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio